AI 驱动科学发现

10000 条实体 · 最后更新 2026-08-31T14:45:33.788Z · cs.LG / q-bio.BM / cond-mat.soft / physics.bio-ph / q-bio.QM / cs.CE / q-bio.OT / cond-mat.stat-mech / cs.ET / q-bio.GN / cond-mat.dis-nn / q-bio.MN

• The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
  AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing the first entirely AI generated peer-review-accepted workshop paper. This system …
  openalex Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu 等 2025-04-10 置信度 0.72 Computer scienceScalabilityArtificial intelligenceTransformative learningScientific discovery
• Artificial intelligence in mental healthcare: transformative potential vs. the necessity of human interaction
  However, while AI offers promise for improving diagnostic precision, it has limitations, as its accuracy depends on the quality of the data it is trained on. Incomplete or biased datasets can lead to significant diagnostic errors, particularly in diverse popul…
  openalex Anithamol Babu, Akhil P. Joseph 2024-12-17 置信度 0.72 Mental healthPsychologyHealth careMedical diagnosisAnxiety
• Generative artificial intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications
  This paper explores recent advancements and implications of artificial intelligence (AI) technology, with a specific focus on Large Language Models (LLMs) like ChatGPT 3.5, within the realm of higher education. Through a review of the academic literature, this…
  openalex Michal Bobula 2024-03-27 置信度 0.72 Generative grammarHigher educationArtificial intelligencePsychologyComputer science
• 2024 roadmap on magnetic microscopy techniques and their applications in materials science
  Abstract Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structura…
  openalex Dennis Valbjørn Christensen, U. Staub, T. R. Devidas, Beena Kalisky 等 2024-03-08 置信度 0.72 NanotechnologyMaterials scienceMicroscopyEngineering physicsEngineering
• Ethical implications of AI in the Metaverse
  Abstract This paper delves into the ethical implications of AI in the Metaverse through the analysis of real-world case studies, including Horizon Worlds, Decentraland, Roblox, Sansar, and Rec Room. The examination reveals recurring concerns related to content…
  openalex Alesia Zhuk 2024-03-13 置信度 0.72 MetaverseSafeguardingModerationCorporate governanceComputer science
• AI-Powered Educational Agents: Opportunities, Innovations, and Ethical Challenges
  Recent advances in large language models (LLMs) have triggered rapid growth in AI-powered educational agents, yet researchers and practitioners still lack a consolidated view of how these systems are engineered and validated. To address this gap, we conducted …
  openalex Diana‐Margarita Córdova‐Esparza 2025-05-31 置信度 0.72 Engineering ethicsPolitical scienceSociologyBusinessEngineering
• Proliferation of AI Tools: A Multifaceted Evaluation of User Perceptions and Emerging Trend
  The rapid advancement of artificial intelligence (AI) technologies, epitomized by tools like ChatGPT, Claude, Bard, Copilot, and Copy AI, has significantly reshaped various professional landscapes. This study aimed to assess the impact of these AI tools on pro…
  openalex Yewande Alice Marquis, Tunbosun Oyewale Oladoyinbo, Samuel Oladiipo Olabanji, Oluwaseun Oladeji Olaniyi 等 2024-01-09 置信度 0.72 WorkforceNoveltySoftware deploymentAdaptation (eye)Workforce development
• The mechanisms of AI hype and its planetary and social costs
  Abstract Our global landscape of emerging technologies is increasingly affected by artificial intelligence (AI) hype, a phenomenon with significant large-scale consequences for the global AI narratives being created today. This paper aims to dissect the phenom…
  openalex Alva Markelius, Connor Wright, Joahna Kuiper, Natalie Delille 等 2024-04-02 置信度 0.72 AstrobiologyComputer scienceData sciencePsychologyPhysics
• Impact of Artificial Intelligence–Based Technology on Nurse Management: A Systematic Review
  Aim: To describe the use of artificial intelligence (AI) by nurse managers to enhance management, leadership, and healthcare outcomes. Background: AI represents a significant transformation in healthcare management by enhancing decision‐making, communication, …
  openalex A. García, Silvia Pérez‐González, Carmen Benavides, Arrate Pinto‐Carral 等 2024-01-01 置信度 0.72 Nursing managementSystematic reviewNursingPsychologyMEDLINE
• AI for chemistry teaching: responsible AI and ethical considerations
  Abstract This paper discusses the ethical considerations surrounding generative artificial intelligence (GenAI) in chemistry education, aiming to guide teachers toward responsible AI integration. GenAI, driven by advanced AI models like Large Language Models, …
  openalex Ron Blonder, Yael Feldman-Maggor 2024-10-15 置信度 0.72 Engineering ethicsChemistryMathematics educationPsychologyEngineering
• Opportunities and challenges in higher education arising from AI: A systematic literature review (2020–2024)
  With society’s continuous development and progress, artificial intelligence (AI) technology is increasingly utilized in higher education, garnering increased attention. The current application of AI in higher education impacts teachers’ instructional methods a…
  openalex Peng‐Fei Cui, Bity Salwana Alias 2024-10-23 置信度 0.72 Systematic reviewEngineering ethicsMedical educationMedicineMEDLINE
• Towards Fair AI: Mitigating Bias in Credit Decisions—A Systematic Literature Review
  The increasing adoption of artificial intelligence algorithms is redefining decision-making across various industries. In the financial sector, where automated credit granting has undergone profound changes, this transformation raises concerns about biases per…
  openalex José Rômulo de Castro Vieira, Flávio Barboza, Daniel O. Cajueiro, Herbert Kimura 2025-04-24 置信度 0.72 Systematic reviewBusinessActuarial scienceEconomicsPolitical science
• From complexity to clarity: How AI enhances perceptions of scientists and the public's understanding of science
  Abstract This article evaluated the effectiveness of using generative AI to simplify science communication and enhance the public's understanding of science. By comparing lay summaries of journal articles from PNAS, yoked to those generated by AI, this work fi…
  openalex David M. Markowitz 2024-09-01 置信度 0.72 CLARITYPerceptionData sciencePsychologyComputer science
• Evaluating machine learning-based intrusion detection systems with explainable AI: enhancing transparency and interpretability
  Machine Learning (ML)-based Intrusion Detection Systems (IDS) are integral to securing modern IoT networks but often suffer from a lack of transparency, functioning as “black boxes” with opaque decision-making processes. This study enhances IDS by integrating …
  openalex Vincent Zibi Mohale, Ibidun Christiana Obagbuwa 2025-05-22 置信度 0.72 InterpretabilityTransparency (behavior)Artificial intelligenceComputer scienceMachine learning
• Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems
  The evolution of the electrical grid from its early centralized structure to today’s advanced “smart grid” reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in efficien…
  openalex T. A. Rajaperumal, C. Christopher Columbus 2025-04-16 置信度 0.72 Sustainable energySmart gridEnergy (signal processing)GridComputer science
• The Mediating Role of Generative AI Self-Regulation on Students’ Critical Thinking and Problem-Solving
  Within the rapid integration of AI into educational settings, understanding its impact on essential cognitive skills is crucial for developing effective teaching strategies and improving student outcomes. This study examines the influence of generative artific…
  openalex Xue Zhou, Da Teng, Hosam Al‐Samarraie 2024-11-27 置信度 0.72 Critical thinkingGenerative grammarPsychologyMathematics educationMetacognition
• Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe
  Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis fo…
  openalex G. Walsh, Nikolaos Stogiannos, Riaan van de Venter, Clare Rainey 等 2023-07-03 置信度 0.72 Multidisciplinary approachScrutinyEngineering ethicsHealth careHealth professionals
• “To Use or Not to Use?” A Mixed-Methods Study on the Determinants of EFL College Learners’ Behavioral Intention to Use AI in the Distributed Learning Context
  Artificial intelligence (AI) offers new possibilities for English as a foreign language (EFL) learners to enhance their learning outcomes, provided that they have access to AI applications. However, little is written about the factors that influence their inte…
  openalex Hanwei Wu, Yunsong Wang, Yongliang Wang 2024-08-26 置信度 0.72 Context (archaeology)PsychologyElectronic learningMathematics educationEducational technology
• Generalizability assessment of AI models across hospitals in a low-middle and high income country
  The integration of artificial intelligence (AI) into healthcare systems within low-middle income countries (LMICs) has emerged as a central focus for various initiatives aiming to improve healthcare access and delivery quality. In contrast to high-income count…
  openalex Jenny Yang, Nguyen Thanh Dung, Phạm Ngọc Thạch, Nguyễn Thanh Phong 等 2024-09-27 置信度 0.72 Generalizability theoryContext (archaeology)Healthcare systemLow and middle income countriesEconomic shortage
• Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases
  Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its pot…
  openalex Uday Pratap Singh Parmar, Pier Luigi Surico, Rohan Bir Singh, Francesco Romano 等 2024-03-23 置信度 0.72 RetinalArtificial intelligenceComputer scienceMedicineOphthalmology
• Generative AI for Cyber Security: Analyzing the Potential of ChatGPT, DALL-E, and Other Models for Enhancing the Security Space
  This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact of cyber threats have continued to rise in today’s world. This ever-evolving threat landscape poses chall…
  openalex Siva Sai, Utkarsh Yashvardhan, Vinay Chamola, Biplab Sikdar 2024-01-01 置信度 0.72 Computer scienceSpace (punctuation)Computer securityOperating system
• Exploring temporal and cross-national patterns: The use of generative AI in science-related information retrieval across seven countries
  This study explores the role of ChatGPT in science-related information retrieval, building on research conducted in 2023. Drawing on online survey data from seven countries—Australia, Denmark, Germany, Israel, South Korea, Taiwan, and the United States—and two…
  openalex Esther Greussing, Lars Guenther, Ayelet Baram‐Tsabari, Shakked Dabran‐Zivan 等 2025-04-14 置信度 0.72 SeekersGenerative grammarGeographyPopulationPhenomenon
• The AI Gambit — Leveraging Artificial Intelligence to Combat Climate Change: Opportunities, Challenges, and Recommendations
  openalex Josh Cowls, Andreas Tsamados, Mariarosaria Taddeo, Luciano Floridi 2021-01-01 置信度 0.72 GambitClimate changeArtificial intelligenceComputer scienceEngineering
• The great AI witch hunt: Reviewers’ perception and (Mis)conception of generative AI in research writing
  Generative AI (GenAI) use in research writing is growing fast. However, it is unclear how peer reviewers recognize or misjudge AI-augmented manuscripts. To investigate the impact of AI-augmented writing on peer reviews, we conducted a snippet-based online surv…
  openalex Hilda Hadan, D. Wang, Reza Hadi Mogavi, Joseph Tu 等 2024-08-01 置信度 0.72 WitchGenerative grammarPerceptionPsychologyCognitive science
• A systematic review of research on AI in language education: Current status and future implications
  Given the rapid advancements in artificial intelligence (AI) technologies for language education, this article provides a review of selected empirical studies on artificial intelligence in language education, spanning from 2013 to October 2023. Data for this r…
  openalex Meina Zhu, Chaoran Wang 2025-01-27 置信度 0.72 Current (fluid)LinguisticsComputer sciencePsychologyMathematics education
• Integrating Omics Data and AI for Cancer Diagnosis and Prognosis
  Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This revie…
  openalex Y. Ozaki, P M Broughton, Hamed Abdollahi, Homayoun Valafar 等 2024-07-03 置信度 0.72 OmicsCancerComputational biologyMedicineBioinformatics
• AI Supported Degradation of the Self Concept: A Theoretical Framework Grounded in Established Cognitive and Computational Mechanisms
  Human feedback is commonly utilized to finetune AI assistants. But human feedback may also encourage model responses that match user beliefs over truthful ones, a behaviour known as sycophancy. We investigate the prevalence of sycophancy in models whose finetu…
  openalex Sharma, Mrinank, Meg Tong, Tomasz Korbak, David Duvenaud 等 2023-10-20 置信度 0.72 PreferenceComputer scienceHuman languagePsychologyCognitive psychology
• AI AND PRODUCT MANAGEMENT: A THEORETICAL OVERVIEW FROM IDEA TO MARKET
  Artificial Intelligence (AI) has emerged as a transformative force in the realm of product management, offering a theoretical framework that reshapes the journey from ideation to market penetration. This abstract provides a comprehensive overview of the theore…
  openalex Damilola Oluwaseun Ogundipe, Sodiq Odetunde Babatunde, Emmanuel Adeyemi Abaku 2024-03-28 置信度 0.72 BusinessComputer science
• Black-Box Access is Insufficient for Rigorous AI Audits
  External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depends on the degree of access granted to auditors. Recent audits of state-of-the-art AI systems have primarily relied on b…
  openalex Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt 等 2024-06-03 置信度 0.72 Black boxAuditComputer scienceBusinessComputer security
• A survey on large language model based autonomous agents
  Abstract Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes,…
  openalex Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang 等 2024-03-22 置信度 0.72 Computer scienceArtificial intelligence
• Perceptions and attitudes of health science students relating to artificial intelligence (AI): A scoping review
  Background and Aims: The recent integration of artificial intelligence (AI) across education, research, and clinical healthcare has led to a growing interest in AI training for healthcare students. This scoping review seeks to delve into existing literature, a…
  openalex Shokoufeh Derakhshanian, Lucy Wood, Elio Arruzza 2024-08-01 置信度 0.72 PerceptionHealth carePsychologyHealth scienceMedical education
• GLAT: The generative AI literacy assessment test
  The rapid integration of generative artificial intelligence (GenAI) technology into education requires precise measurement of GenAI literacy to ensure that learners and educators possess the skills to engage with and critically evaluate this transformative tec…
  openalex Yueqiao Jin, Roberto Martínez‐Maldonado, Dragan Gašević, Lixiang Yan 2025-06-09 置信度 0.72 Generative grammarTest (biology)LiteracyComputer scienceNatural language processing
• Complete AI-Enabled Echocardiography Interpretation With Multitask Deep Learning
  Importance: Echocardiography is a cornerstone of cardiovascular care, but relies on expert interpretation and manual reporting from a series of videos. An artificial intelligence (AI) system, PanEcho, has been proposed to automate echocardiogram interpretation…
  openalex Gregory Holste, Evangelos Oikonomou, Márton Tokodi, Attila Kovács 等 2025-06-23 置信度 0.72 MedicineInterpretation (philosophy)Artificial intelligenceDeep learningMulti-task learning
• Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education
  The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, su…
  openalex Lukas Weidener, Michael Fischer 2024-01-29 置信度 0.72 BeneficenceEngineering ethicsMedical ethicsAutonomyContext (archaeology)
• AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting
  Advances in generative artificial intelligence show great potential for improving education. Yet little is known about how this new technology should be used and how effective it can be compared to current best practices. Here we report a randomized, controlle…
  openalex Greg Kestin, Kelly Miller, Anna Klales, Timothy Milbourne 等 2025-06-03 置信度 0.72 TUTORClass (philosophy)Computer scienceActive learning (machine learning)Generative grammar
• Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review
  BACKGROUND: Artificial intelligence (AI)-based systems are being rapidly integrated into the fields of health and social care. Although such systems can substantially improve the provision of care, diverse and marginalized populations are often incorrectly or …
  openalex John Marko, Ciprian Daniel Neagu, P. B. Anand 2025-02-05 置信度 0.72 Equity (law)Health careInclusion (mineral)Health equitySystematic review
• The Mediating Effect of AI Trust on AI Self-Efficacy and Attitude Toward AI of College Students
  This quantitative study investigated the mediating effect of AI trust on the relationship between AI self-efficacy and attitude toward AI of college students in Region XI, Philippines. Using adapted questionnaires, the data were gathered online via Google Form…
  openalex Brandon Obenza, Jasper Simon Ian E Baguio, Karyl Maxine W Bardago, Lemuel B Granado 等 2023-12-30 置信度 0.72 MediationBootstrapping (finance)PsychologySelf-efficacyStratified sampling
• Responsible AI Practice in Libraries and Archives
  Artificial intelligence (AI) has the potential to positively impact library and archives collections and services—enhancing reference, instruction, metadata creation, recommendations, and more. However, AI also has ethical implications. This paper presents an …
  openalex Sara Mannheimer, Natalie Bond, Scott W. H. Young, Hannah Scates Kettler 等 2024-09-23 置信度 0.72 Computer scienceWorld Wide WebLibrary scienceInformation retrieval
• User perceptions and experiences of an AI-driven conversational agent for mental health support
  Background: The increasing prevalence of artificial intelligence (AI)-driven mental health conversational agents necessitates a comprehensive understanding of user engagement and user perceptions of this technology. This study aims to fill the existing knowled…
  openalex Beenish Moalla Chaudhry, Happy Rani Debi 2024-07-01 置信度 0.72 PerceptionMental healthDialog systemHuman–computer interactionComputer science
• The Role of IT Governance in the Integration of AI in Accounting and Auditing Operations
  IT governance is a framework that manages the efficient use of information technology within an organization, focusing on strategic alignment, risk management, resource management, performance measurement, compliance, and value delivery. This study investigate…
  openalex Faozi A. Almaqtari 2024-08-01 置信度 0.72 AccountingAuditCorporate governanceAccounting information systemBusiness
• The Metaverse: Innovations and generative AI
  Today, the Metaverse consists of various platforms, including digital twins of the physical world as well as virtual and blended digital-material environments that offer immersive experiences for individual users. By going beyond solely physical or virtual rea…
  openalex Jussi S. Jauhiainen 2024-04-17 置信度 0.72 MetaverseGenerative grammarThrivingComputer scienceCreativity
• Digital Mirrors: AI Companions and the Self
  This exploratory study examines the socio-technical dynamics of Artificial Intelligence Companions (AICs), focusing on user interactions with AI platforms like Replika 9.35.1. Through qualitative analysis, including user interviews and digital ethnography, we …
  openalex Theodoros Kouros, Venetia Papa 2024-10-08 置信度 0.72 Computer scienceAstrobiologyComputer graphics (images)Physics
• Re-emergence of Oropouche virus between 2023 and 2024 in Brazil: an observational epidemiological study
  Background Oropouche virus is an arthropod-borne virus that has caused outbreaks of Oropouche fever in central and South America since the 1950s. This study investigates virological factors contributing to the re-emergence of Oropouche fever in Brazil between …
  openalex Gabriel C Scachetti, Julia Forato, Ingra Morales Claro, Xinyi Hua 等 2024-10-15 置信度 0.72 Observational studyEpidemiologyEnvironmental healthVirologyGeography
• A Survey of Large Language Models
  Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advan…
  openalex Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang 等 2026-05-09 置信度 0.72 Language modelComputer scienceMainstreamScale (ratio)Artificial intelligence
• Abundant intelligences: placing AI within Indigenous knowledge frameworks
  The current trajectory of artificial intelligence development suffers from fundamental epistemological shortcomings, resulting in the systematic operationalization of bias against non-white, non-male, and non-Western peoples. We argue that these failings are, …
  openalex Jason Edward Lewis, Hēmi Whaanga, Ceyda Yolgörmez 2024-10-19 置信度 0.72 IndigenousPerforming artsTheory of multiple intelligencesTraditional knowledgeKnowledge management
• The carbon emissions of writing and illustrating are lower for AI than for humans
  As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. In this article, we present a comparative analysis of the carbon emissions associated with AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) and…
  openalex Bill Tomlinson, Rebecca W. Black, Donald J. Patterson, Andrew W. Torrance 2024-02-14 置信度 0.72 Greenhouse gasPrinciple of legalityComputer scienceGreenhouse effectNatural resource economics
• ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy
  This study sought to understand students' intentions regarding the use of ChatGPT in learning from the perspective of perceived value, exploring the influence of artificial intelligent (AI) literacy. Drawing on a sample of 676 university students from diverse …
  openalex Ahlam Mohammed Al-Abdullatif, Merfat Ayesh Alsubaie 2024-09-19 置信度 0.72 PerceptionPsychologyStructural equation modelingValue (mathematics)Curriculum
• AI in the classroom: Exploring students’ interaction with ChatGPT in programming learning
  Abstract As being more prevalent in educational settings, understanding the impact of artificial intelligence tools on student behaviors and interactions has become crucial. In this regard, this study investigates the dynamic interactions between students and …
  openalex Hacer Güner, Erkan Er 2025-01-15 置信度 0.72 Educational technologyMathematics educationComputer scienceTechnology integrationPsychology
• The future of self-driving laboratories: from human in the loop interactive AI to gamification
  Self-driving laboratories (SDLs) are the future for scientific discovery in a world growing with artificial intelligence. The interaction between scientists and automated instrumentation are leading conversations about the impact of SDLs on research.
  openalex Holland Hysmith, Elham Foadian, Shakti P. Padhy, Sergei V. Kalinin 等 2024-01-01 置信度 0.72 Instrumentation (computer programming)Human–computer interactionComputer scienceOperating system
• Toward Intelligent Monitoring in IoT: AI Applications for Real-Time Analysis and Prediction
  In the contemporary era, the intersection of the Internet of Things and artificial intelligence revolutionizes how industries monitor and optimize their operations. In this work, we present a system that combines real-time monitoring provided by Internet of Th…
  openalex William Villegas-Ch, Joselin Garcí­a-Ortiz, Santiago Sánchez-Viteri 2024-01-01 置信度 0.72 Computer scienceInternet of ThingsReal-time computingArtificial intelligenceData mining
• AI and Ethics: A Systematic Review of the Ethical Considerations of Large Language Model Use in Surgery Research
  INTRODUCTION: As large language models receive greater attention in medical research, the investigation of ethical considerations is warranted. This review aims to explore surgery literature to identify ethical concerns surrounding these artificial intelligenc…
  openalex Sophia M. Pressman, Sahar Borna, Cesar A. Gomez-Cabello, Syed Ali Haider 等 2024-04-13 置信度 0.72 BeneficenceAutonomyDignityConfidentialityEngineering ethics
• Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels
  BACKGROUND: Artificial intelligence (AI) applications present opportunities to enhance the diagnosis, prognosis, and treatment of various diseases. To successfully integrate and utilize AI in healthcare, it is crucial to understand the perspectives of healthca…
  openalex Dennis Henzler, Sebastian Schmidt, Ayca Koçar, Sophie Herdegen 等 2025-05-01 置信度 0.72 Health informaticsHealth administrationNursing researchMedicineHealth care
• Transformative AI in human resource management: enhancing workforce planning with topic modeling
  This study explores the transformative role of artificial intelligence (AI) in human resource management (HRM), focusing on key functions such as recruitment, retention, and performance management. A comprehensive review was carried out PRISMA framework and BE…
  openalex Murale Venugopal, Vandana Madhavan, Rajiv Prasad, Raghu Raman 2024-11-26 置信度 0.72 Transformative learningWorkforceKnowledge managementBusinessWorkforce planning
• Advancing the Search Frontier with AI Agents
  Dek : AI agents are extending the capabilities of traditional search engines to help users tackle complex tasks.
  openalex Ryen W. White 2024-08-20 置信度 0.72 FrontierComputer sciencePolitical scienceLaw
• Can digital leadership transform AI anxiety and attitude in nurses?
  BACKGROUND: The lack of artificial intelligence applications in nursing education and the nursing profession in Turkey and the need for strategies for integrating artificial intelligence into the nursing profession continues. At this point, there is a need to …
  openalex Sinan Tarsuslu, Ferhat Onur Ağaoğlu, Murat Baş 2024-07-31 置信度 0.72 AnxietyNursingPerceptionPsychologyScale (ratio)
• TOWARDS A CONCEPTUAL FRAMEWORK FOR ETHICAL AI DEVELOPMENT IN IT SYSTEMS
  The rapid advancement of artificial intelligence (AI) technologies has prompted significant societal, ethical, and legal concerns regarding their deployment in information technology (IT) systems. Addressing these concerns necessitates the establishment of a r…
  openalex Oluwabukunmi Latifat Olorunfemi, Olukunle Oladipupo Amoo, Akoh Atadoga, Oluwatoyin Ajoke Fayayola 等 2024-03-18 置信度 0.72 Conceptual frameworkEngineering ethicsKnowledge managementProcess managementComputer science
• Standards, frameworks, and legislation for artificial intelligence (AI) transparency
  openalex Brady Lund, Zeynep Orhan, Nishith Reddy Mannuru, Ravi Varma Kumar Bevara 等 2025-01-29 置信度 0.72 Transparency (behavior)LegislationComputer scienceBusinessArtificial intelligence
• Evidence of a social evaluation penalty for using AI
  Despite the rapid proliferation of AI tools, we know little about how people who use them are perceived by others. Drawing on theories of attribution and impression management, we propose that people believe they will be evaluated negatively by others for usin…
  openalex Jessica Reif, Richard P. Larrick, Jack B. Soll 2025-05-08 置信度 0.72 AttributionDilemmaAffect (linguistics)Competence (human resources)Psychology
• Utilizing customized CNN for brain tumor prediction with explainable AI
  Timely diagnosis of brain tumors using MRI and its potential impact on patient survival are critical issues addressed in this study. Traditional DL models often lack transparency, leading to skepticism among medical experts owing to their "black box" nature. T…
  openalex Md. Imran Nazir, Afsana Akter, Md. Anwar Hussen Wadud, Md. Ashraf Uddin 2024-10-01 置信度 0.72 Artificial intelligenceComputer science
• The Impact of Explainable AI on Teachers’ Trust and Acceptance of AI EdTech Recommendations: The Power of Domain-specific Explanations
  Trust is crucial for teachers’ adoption of AI-enhanced educational technologies (AI-EdTech), yet how this trust is formed and maintained remains poorly understood. An aspect of the system design that seems profoundly related to trust is transparency, which can…
  openalex Yael Feldman-Maggor, Mutlu Cukurova, Carmel Kent, Giora Alexandron 2025-06-16 置信度 0.72 Domain (mathematical analysis)Educational technologyPower (physics)PsychologyComputer science
• Unleashing the Potential of Generative AI, Conversational Agents and Chatbots in Educational Praxis: A Systematic Review and Bibliometric Analysis of GenAI in Education
  In the rapidly evolving landscape of education, the pivotal axis around which transformation revolves is human-AI interaction. In this sense, this paper adopts a data mining and analytic approach to understand what the related literature tells us regarding the…
  openalex Aras Bozkurt 2023-01-01 置信度 0.72 PraxisGenerative grammarComputer scienceSociologyData science
• Diversity and Standards in Writing for Publication in the Age of AI—Between a Rock and a Hard Place
  Abstract Research communities across disciplines recognize the need to diversify and decolonize knowledge. While artificial intelligence-supported large language models (LLMs) can help with access to knowledge generated in the Global North and demystify public…
  openalex Maria Kuteeva, Marta Andersson 2024-03-21 置信度 0.72 Diversity (politics)PsychologyLinguisticsSociologyPhilosophy
• Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework
  In diverse classrooms, one of the challenges educators face is creating assessments that reflect the different cultural backgrounds of every student. This study presents a novel approach to the automatic generation of cultural and context-specific science asse…
  openalex Matthew Nyaaba, Xiaoming Zhaı, Morgan Z. Faison 2024-11-30 置信度 0.72 Generative grammarScience educationComputer scienceConcept learningMathematics education
• Distinguishing Reality from AI: Approaches for Detecting Synthetic Content
  The advancement of artificial intelligence (AI) technologies, including generative pre-trained transformers (GPTs) and generative models for text, image, audio, and video creation, has revolutionized content generation, creating unprecedented opportunities and…
  openalex David Ghiurău, Daniela Elena Popescu 2024-12-24 置信度 0.72 Content (measure theory)Computer scienceArtificial intelligenceMathematicsMathematical analysis
• Past, present and future of AI in marketing and knowledge management
  Purpose This paper aims to explore the intersection of artificial intelligence (AI) and marketing within the context of knowledge management (KM). It investigates how AI technologies facilitate data-driven decision-making, enhance business communication, impro…
  openalex Reza Marvi, Pantea Foroudi, Maria Teresa Cuomo 2024-12-16 置信度 0.72 Knowledge managementMarketing researchComputer sciencePersonalizationMarketing management
• Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance
  Abstract With the continuous development of technological and educational innovation, learners nowadays can obtain a variety of supports from agents such as teachers, peers, education technologies, and recently, generative artificial intelligence such as ChatG…
  openalex Yizhou Fan, Luzhen Tang, Huixiao Le, Kejie Shen 等 2024-12-10 置信度 0.72 MetacognitionArtificial intelligenceTask (project management)LazinessComputer science
• Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine
  Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artific…
  openalex Peng Zhang, Dingfan Zhang, Wuai Zhou, Lan Wang 等 2023-11-22 置信度 0.72 Perspective (graphical)Computer scienceArtificial intelligenceTraditional Chinese medicineMechanism (biology)
• AI in imaging: the regulatory landscape
  Artificial intelligence (AI) methods have been applied to medical imaging for several decades, but in the last few years, the number of publications and the number of AI-enabled medical devices coming on the market have significantly increased. While some AI-e…
  openalex David Hill 2024-01-04 置信度 0.72 RigourField (mathematics)Computer scienceStrengths and weaknessesQuality (philosophy)
• Modeling Teachers’ Acceptance of Generative Artificial Intelligence Use in Higher Education: The Role of AI Literacy, Intelligent TPACK, and Perceived Trust
  This study delves into the factors that drive teachers’ adoption of generative artificial intelligence (GenAI) technologies in higher education. Anchored by the technology acceptance model (TAM), the research expands its inquiry by integrating the constructs o…
  openalex Ahlam Mohammed Al-Abdullatif 2024-11-03 置信度 0.72 LiteracyPsychologyGenerative grammarComputer scienceMathematics education
• Writing without borders: AI and cross-cultural convergence in academic writing quality
  English has become the dominant language in global academic publishing, facilitating cross-border collaboration while reinforcing structural barriers for non-native English-speaking researchers. This study examines the evolution of academic writing quality in …
  openalex Arjun Prakash, Shruti Aggarwal, Jeevan John Varghese, Joel John Varghese 2025-07-09 置信度 0.72 Convergence (economics)Quality (philosophy)Computer scienceMathematics educationPsychology
• Empowering personalized pharmacogenomics with generative AI solutions
  OBJECTIVE: This study evaluates an AI assistant developed using OpenAI's GPT-4 for interpreting pharmacogenomic (PGx) testing results, aiming to improve decision-making and knowledge sharing in clinical genetics and to enhance patient care with equitable acces…
  openalex Mullai Murugan, Bo Yuan, Eric Venner, Christie M. Ballantyne 等 2024-02-20 置信度 0.72 PharmacogenomicsGenerative grammarPersonalized medicineComputer scienceArtificial intelligence
• AI Methods for Antimicrobial Peptides: Progress and Challenges
  Antimicrobial peptides (AMPs) are promising candidates to combat multidrug-resistant pathogens. However, the high cost of extensive wet-lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) tech…
  openalex Carlos A. Brizuela, Gary Liu, Jonathan M. Stokes, César de la Fuente‐Núñez 2025-01-01 置信度 0.72 Computer scienceData scienceAntimicrobial peptidesDrug discoveryField (mathematics)
• The Impact of AI Scribes on Streamlining Clinical Documentation: A Systematic Review
  Background: Burnout among clinicians, including physicians, is a growing concern in healthcare. An overwhelming burden of clinical documentation is a significant contributor. While medical scribes have been employed to mitigate this burden, they have limitatio…
  openalex Maxime Sasseville, Farzaneh Yousefi, Steven Ouellet, Florian Naye 等 2025-06-16 置信度 0.72 DocumentationGeneralizability theoryWorkflowHealth careMedicine
• Generative artificial intelligence
  Abstract Recent developments in the field of artificial intelligence (AI) have enabled new paradigms of machine processing, shifting from data-driven, discriminative AI tasks toward sophisticated, creative tasks through generative AI. Leveraging deep generativ…
  openalex Leonardo Banh, Gero Strobel 2023-12-01 置信度 0.72 Generative grammarComputer scienceArtificial intelligenceField (mathematics)Generative model
• Generative AI for growth hacking: How startups use generative AI in their growth strategies
  • GenAI-driven growth hacking powers startup product development, sales and operations. • GenAI drives product-led growth by technical and non-technical content creation. • LLMs streamline sales-led growth in content repurposing and customer personalization. •…
  openalex Arash Rezazadeh, Marco Kohns, René Bohnsack, Nuno António 等 2025-03-21 置信度 0.72 Generative grammarHackerGenerative modelComputer scienceArtificial intelligence
• Personalized cancer vaccine design using AI-powered technologies
  Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer cells. Wh…
  openalex Anant Kumar, Shriniket Dixit, Kathiravan Srinivasan, M. Dinakaran 等 2024-11-07 置信度 0.72 CancerCancer vaccineCancer immunotherapyMedicinePrecision medicine
• Omega-3 world map: 2024 update
  In 2016, the first worldwide n3 PUFA status map was published using the Omega-3 Index (O3I) as standard biomarker. The O3I is defined as the percentage of EPA + DHA in red blood cell (RBC) membrane FAs. The purpose of the present study was to update the 2016 m…
  openalex Jan Philipp Schuchardt, Philine Beinhorn, Xue Feng Hu, Hing Man Chan 等 2024-06-13 置信度 0.72 Observational studyGeographyBaseline (sea)BiomarkerDemography
• AI-enhanced biomedical micro/nanorobots in microfluidics
  recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidati…
  openalex Hui Dong, Jiawen Lin, Yihui Tao, Jia Yuan 等 2024-01-01 置信度 0.72 NanoroboticsMicrofluidicsNanotechnologyEnhanced Data Rates for GSM EvolutionEngineering
• Attitudes, perceptions and AI self-efficacy in K-12 education
  Access to AI-driven chatbots is triggering schools to transform. Easy access and questions of cheating are balanced against potential upsides of individual support, time savings, and the risk of falling behind. Therefore, insights into teachers’ AI self-effica…
  openalex Nina Bergdahl, Jeanette Sjöberg 2025-01-05 置信度 0.72 Self-efficacyPsychologyPerceptionSocial psychologyMedical education
• All Eyez on AI: A Roadmap for Science Communication Research in the Age of Artificial Intelligence
  Artificial Intelligence (AI) is profoundly reshaping the field of science communication research. We conducted a literature review of 35 articles published between 2002 and 2024, which reveals that research on AI in science communication is still in its infanc…
  openalex Sabrina Heike Kessler, Daniela Mahl, Mike S. Schäfer, Sophia Charlotte Volk 2025-04-14 置信度 0.72 Artificial intelligenceComputer scienceData scienceManagement scienceEngineering
• AI and biosecurity: The need for governance
  Governments should evaluate advanced models and if needed impose safety measures.
  openalex Doni Bloomfield, Jaspreet Pannu, Alex Zhu, Madelena Y. Ng 等 2024-08-22 置信度 0.72 BiosecurityCorporate governanceBusinessEnvironmental planningRisk analysis (engineering)
• The Medical Segmentation Decathlon
  International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on…
  openalex Michela Antonelli, Annika Reinke, Spyridon Bakas, Keyvan Farahani 等 2022-07-15 置信度 0.72 Generalizability theorySegmentationComputer scienceTask (project management)Set (abstract data type)
• A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation
  Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biolo…
  openalex Xiangru Tang, Howard Dai, Elizabeth Knight, Fang Wu 等 2024-05-23 置信度 0.72 Computer scienceGenerative grammarSynthetic biologyProcess (computing)Macro
• Strategic goals for artificial intelligence integration among STEM academics and undergraduates in African higher education: a systematic review
  There is a strong demand for science, technology, engineering, and mathematics (STEM) education in Africa, which is crucial in driving the industrial revolution. Efforts to promote STEM education have led to innovations in the teaching–learning process where e…
  openalex Oluwanife Segun Falebita, Petrus Jacobus Kok 2024-09-13 置信度 0.72 Higher educationEngineering ethicsPsychologyPolitical scienceEngineering
• Applications of artificial intelligence (AI) in managing food quality and ensuring global food security
  The food industry uses artificial intelligence (AI) to enhance food quality and security while proposing significant capital savings and resource optimization. Additionally, understanding machine learning (ML) techniques is essential for their effectiveness. T…
  openalex Ali Ikram, Hassan Mehmood, Muhammad Tayyab Arshad, Areeba Rasheed 等 2024-09-09 置信度 0.72 Food securityBusinessQuality (philosophy)Food qualityComputer science
• Improving Educational Outcomes Through Adaptive Learning Systems using AI
  Adaptive learning systems powered by AI have transformed education by offering personalized learning experiences tailored to individual student needs, enhancing engagement and outcomes. This study examines the impact of AI-driven adaptive learning systems on e…
  openalex Herva Emilda Sari, Benelekser Tumanggor, David Efron 2024-11-06 置信度 0.72 Adaptive learningComputer scienceArtificial intelligencePsychologyMachine learning
• A review of evaluation approaches for explainable AI with applications in cardiology
  Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context o…
  openalex Ahmed Salih, Ilaria Boscolo Galazzo, Polyxeni Gkontra, Elisa Rauseo 等 2024-08-09 置信度 0.72 Computer scienceMedical physicsCardiologyInternal medicineArtificial intelligence
• Collective Constitutional AI: Aligning a Language Model with Public Input
  There is growing consensus that language model (LM) developers should not be the sole deciders of LM behavior, creating a need for methods that enable the broader public to collectively shape the behavior of LM systems that affect them. To address this need, w…
  openalex Saffron Huang, Divya Siddarth, Liane Lovitt, Thomas I. Liao 等 2024-06-03 置信度 0.72 Cognitive reframingComputer scienceBaseline (sea)Language modelProcess (computing)
• On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values
  Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) are globally occurring natural hazards which pose great threats to our society, leading to fatalities and economical losses.…
  openalex Nan Wang, Hongyan Zhang, Ashok Dahal, Weiming Cheng 等 2024-02-01 置信度 0.72 Context (archaeology)Computer scienceInterpretation (philosophy)Artificial intelligenceSpatial contextual awareness
• Teaming Up with an AI: Exploring Human–AI Collaboration in a Writing Scenario with ChatGPT
  Recent advancements in artificial intelligence (AI) technologies, particularly in generative pre-trained transformer large language models, have significantly enhanced the capabilities of text-generative AI tools—a development that opens new avenues for human–…
  openalex Teresa Luther, Joachim Kimmerle, Ulrike Creß 2024-08-05 置信度 0.72 Computer sciencePsychology
• Advancing precision oncology with AI-powered genomic analysis
  Multiomics data integration approaches offer a comprehensive functional understanding of biological systems, with significant applications in disease therapeutics. However, the quantitative integration of multiomics data presents a complex challenge, requiring…
  openalex Ruby Srivastava 2025-04-30 置信度 0.72 Precision medicineGenomicsBiomarker discoveryData integrationOmics
• Performance of three artificial intelligence (AI)‐based large language models in standardized testing; implications for AI‐assisted dental education
  INTRODUCTION: The emerging rise in novel computer technologies and automated data analytics has the potential to change the course of dental education. In line with our long-term goal of harnessing the power of AI to augment didactic teaching, the objective of…
  openalex Hamoun Sabri, Muhammad H. A. Saleh, Parham Hazrati, K. Merchant 等 2024-07-18 置信度 0.72 Dental educationMedical educationMedicinePsychologyArtificial intelligence
• Soil Science-Informed Machine Learning
  Machine learning (ML) applications in soil science have significantly increased over the past two decades, reflecting a growing trend towards data-driven research addressing soil security. This extensive application has mainly focused on enhancing predictions …
  openalex Budiman Minasny, Toshiyuki Bandai, Teamrat A. Ghezzehei, Yin‐Chung Huang 等 2024-11-13 置信度 0.72 Computer scienceEnvironmental scienceSoil science
• Anatomy of an AI-powered malicious social botnet
  Large language models (LLMs) exhibit impressive capabilities in generating realistic text across diverse subjects. Concerns have been raised that they could be utilized to produce fake content with a deceptive intention, although evidence thus far remains anec…
  openalex Kai‐Cheng Yang, Filippo Menczer 2024-05-29 置信度 0.72 BotnetAnatomyComputer securityComputer scienceBiology
• Exploring the Impact of Generative AI ChatGPT on Critical Thinking in Higher Education: Passive AI-Directed Use or Human–AI Supported Collaboration?
  Generative AI is weaving into the fabric of many human aspects through its transformative power to mimic human-generated content. It is not a mere technology; it functions as a generative virtual assistant, raising concerns about its impact on cognition and cr…
  openalex Nesma Ragab Nasr, Chih‐Hsiung Tu, Jennifer Werner, Tonia Bauer 等 2025-09-11 置信度 0.72 Transformative learningCritical thinkingCognitionGenerative grammarPsychology
• Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
  Recent advances in AI technologies have notably expanded device intelligence, fostering federation and cooperation among distributed AI agents. These advancements impose new requirements on future 6G mobile network architectures. To meet these demands, it is e…
  openalex Emilio Calvanese Strinati, Paolo Di Lorenzo, Vincenzo Sciancalepore, Adnan Aijaz 等 2024-06-03 置信度 0.72 Computer scienceArtificial intelligenceNatural language processing
• Ethics-based AI auditing: A systematic literature review on conceptualizations of ethical principles and knowledge contributions to stakeholders
  This systematic literature review synthesizes the conceptualizations of ethical principles in AI auditing literature and the knowledge contributions to the stakeholders of AI auditing. We explain how the literature discusses fairness, transparency, non-malefic…
  openalex Joakim Laine, Matti Minkkinen, Matti Mäntymäki 2024-05-01 置信度 0.72 AuditEngineering ethicsKnowledge managementSociologyPsychology
• Generative AI model privacy: a survey
  Abstract The rapid progress of generative AI models has yielded substantial breakthroughs in AI, facilitating the generation of realistic synthetic data across various modalities. However, these advancements also introduce significant privacy risks, as the mod…
  openalex Yihao Liu, Jinhe Huang, Yanjie Li, Dong Wang 等 2024-12-04 置信度 0.72 Generative grammarComputer scienceIntersection (aeronautics)Generative modelModalities
• Unmasking inequalities of the code: Disentangling the nexus of AI and inequality
  openalex Tuba Bircan, Mustafa F. Özbilgin 2024-12-02 置信度 0.72 Nexus (standard)InequalityCode (set theory)SociologyComputer science
• An Overview of Tools and Technologies for Anxiety and Depression Management Using AI
  This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and ass…
  openalex Adrianos Pavlopoulos, Theodoros Rachiotis, Ilias Maglogiannis 2024-10-08 置信度 0.72 Depression (economics)AnxietyPsychologyComputer sciencePsychiatry
• Protein Structure Prediction
  crossref Qingfeng Chen 2024-04-25T11:01:47Z 置信度 0.70
• Protein–ligand structure prediction
  crossref Rita Strack 2024-04-12T16:02:51Z 置信度 0.70
• Protein Structure Prediction and Analysis
  crossref Luisa Di Paola 2024-06-03T21:01:36Z 置信度 0.70
• Accurate Prediction of Protein-Binding Residues in Protein Sequences Using SCRIBER
  Deciphering molecular-level mechanisms that govern protein-protein interactions (PPIs) relies in part on the accurate prediction of protein-binding partners and protein-binding residues. These predictions can be used to support a wide spectrum of applications …
  crossref Jian Zhang, Feng Zhou, Xingchen Liang, Lukasz Kurgan 2024-11-22T10:42:07Z 置信度 0.70
• Structural Bioinformatics and Protein Structure Prediction
  crossref Kavita Patel, Ashutosh Mani 2024-09-21T16:02:02Z 置信度 0.70
• Structure prediction of alternative protein conformations
  Abstract Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins with conformations most likely to exist in the PDB. Howeve…
  crossref Patrick Bryant, Frank Noé 2024-08-26T06:02:23Z 置信度 0.70
• From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks
  Recent advancements in artificial intelligence (AI) and deep learning have revolutionized the field of protein engineering, particularly in the area of de novo protein design. This review article explores the impact of AI-driven approaches on protein design, w…
  crossref Marcelo Kauffman 2024-04-03T04:11:57Z 置信度 0.70
• Improved prediction of site‐rates from structure with averaging across homologs
  Abstract Variation in mutation rates at sites in proteins can largely be understood by the constraint that proteins must fold into stable structures. Models that calculate site‐specific rates based on protein structure and a thermodynamic stability model have …
  crossref Christoffer Norn, Fábio Oliveira, Ingemar André 2024-06-25T04:16:24Z 置信度 0.70
• Prediction and Evaluation of Protein Aggregation with Computational Methods
  Protein and peptide aggregation has recently become one of the most studied biomedical problems due to its central role in several neurodegenerative disorders and of biotechnological importance. Multiple in silico methods, databases, tools, and algorithms have…
  crossref Mubashir Hassan, Saba Shahzadi, Mai Suan Li, Andrzej Kloczkowski 2024-11-22T10:42:33Z 置信度 0.70
• DescribePROT Database of Residue-Level Protein Structure and Function Annotations
  DescribePROT is a freely available online database of structural and functional descriptors of proteins at the amino acid level. It provides access to 13 diverse descriptors that include sequence conservation, putative secondary structure, solvent accessibilit…
  crossref Bi Zhao, Sushmita Basu, Lukasz Kurgan 2024-11-22T05:44:18Z 置信度 0.70
• Prediction of Oligomeric Status of Quaternary Protein Structure by using Sequential Minimal Optimization for Support Vector Machine
  Abstract The prediction of quaternary structure and its function is important in pharmaceutical applications, drug design, medical or engineering applications. With the drastic rise in the number of protein sequences presented to the public database, acquiring…
  crossref Suresh Kumar 2024-08-17T19:20:13Z 置信度 0.70
• Creators of AI protein-structure prediction win chemistry Nobel
  The 2024 Nobel Prize for Chemistry has been awarded to David Baker, Demis Hassibis and John Jumper for their work on protein structure and design.
  crossref Hamish Johnston 2024-12-10T11:20:30Z 置信度 0.70
• Computational Prediction of Linear Interacting Peptides
  Intrinsically disordered protein regions, IDRs, are observed in many eukaryotic proteins. They play critical roles in essentially all cellular processes because segments of these regions, known as linear interacting peptides (LIPs), are heavily involved in reg…
  crossref Nawar Malhis, Jörg Gsponer 2024-11-22T05:44:53Z 置信度 0.70
• Peer Review #3 of "Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential (v0.1)"
  Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities.PNV is useful for raising public awareness about land degradation and for estimating land potential…
  crossref 2018-08-27T02:31:45Z 置信度 0.70
• Peer Review #1 of "Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential (v0.1)"
  Potential Natural Vegetation (PNV) is the vegetation cover in equilibrium with climate, that would exist at a given location if not impacted by human activities.PNV is useful for raising public awareness about land degradation and for estimating land potential…
  crossref 2018-08-27T02:31:57Z 置信度 0.70
• Exploring the Potential of Machine Learning in Agriculture: A Review of its Applications and Results
  This review paper provides an overview of the applications of machine learning in the agriculture field. Machine learning, a subfield of artificial intelligence, has been successfully applied to various domains, and agriculture is no exception. The paper start…
  crossref Barkha Bhardwaj, Shivam Tiwari 2023-02-22T11:39:47Z 置信度 0.70
• Review for "Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals"
  crossref 2022-08-08T09:04:55Z 置信度 0.70
• Mapping Almond Stem Water Potential using Machine Learning
  Abstract Almonds are a major crop in California which produces 80% of all the world’s almonds. Widespread drought and strict groundwater regulations pose significant challenges to growers. Irrigation regimes based on observed crop water status can help to opti…
  crossref Peter Savchik, Mallika Nocco, Isaya Kisekka 2023-03-30T22:15:28Z 置信度 0.70
• Review for "Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals"
  crossref 2022-08-08T09:04:55Z 置信度 0.70
• Protein structure prediction in the era of AI: challenges and limitations when applying to <i>in-silico</i> force spectroscopy
  Mechanoactive proteins are essential for a myriad of physiological and pathological processes. Guided by the advances in single-molecule force spectroscopy (SMFS), we have reached a molecular-level understanding of how several mechanoactive proteins respond to…
  preprints 2022 置信度 0.74
• Protein Structure Prediction Using a Maximum Likelihood Formulation of a Recurrent Geometric Network
  Only ∼40% of the human proteome has structural coordinates available from experiment ( i . e ., X-ray crystallography, NMR spectroscopy, or cryo-EM) or homology modeling with quality templates ( i . e ., 30% sequence identity or greater), leaving most of the p…
  preprints 2021 置信度 0.74
• Construct a variable-length fragment library for <i>de novo</i> protein structure prediction
  Although remarkable achievements, such as AlphaFold2, have been made in end-to-end structure prediction, fragment libraries remain essential for de novo protein structure prediction, which can help explore and understand the protein-folding mechanism. In this …
  preprints 2022 置信度 0.74
• Decoding microbiome and protein family linkage to improve protein structure prediction
  Information extracted from microbiome sequences through deep-learning techniques can significantly improve protein structure and function modeling. However, the model training and metagenome search were largely blind with low efficiency. Built on 4.25 billion …
  preprints 2021 置信度 0.74
• Protein language model embeddings for fast, accurate, alignment-free protein structure prediction
  All state-of-the-art (SOTA) protein structure predictions rely on evolutionary information captured in multiple sequence alignments (MSAs), primarily on evolutionary couplings (co-evolution). Such information is not available for all proteins and is computatio…
  preprints 2021 置信度 0.74
• Single-sequence protein structure prediction using language models from deep learning
  ABSTRACT AlphaFold2 and related systems use deep learning to predict protein structure from co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite dramatic, recent increases in accuracy, three challenges remain: (i) prediction of…
  preprints 2021 置信度 0.74
• Course-based Undergraduate Research Module for Enzyme Discovery Using Protein Structure Prediction
  A major challenge for science educators is teaching foundational concepts while introducing their students to current research. Here we describe an active learning module developed to teach protein structure fundamentals while supporting ongoing research in en…
  preprints 2021 置信度 0.74
• A Sequential Niche Multimodal Conformation Sampling Algorithm for Protein Structure Prediction
  Motivation Massive local minima on the protein energy surface often causes traditional conformation sampling algorithms to be easily trapped in local basin regions, because they are difficult to stride over high-energy barriers. Also, the lowest energy conform…
  preprints 2020 置信度 0.74
• Pseudocontact shift NMR data obtained from a non-canonical amino acid-linked lanthanide tag improves integral membrane protein structure prediction
  A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural informati…
  preprints 2022 置信度 0.74
• Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction
  Among a wide variety of mass spectrometry (MS) methodologies available for structural characterizations of proteins, ion mobility (IM) provides structural information about protein shape and size in the form of an orientationally averaged collision cross-secti…
  preprints 2021 置信度 0.74
• De novo protein structure prediction by incremental inter-residue geometries prediction and model quality assessment using deep learning
  Motivation The successful application of deep learning has promoted progress in protein model quality assessment. How to use model quality assessment to further improve the accuracy of protein structure prediction, especially not reliant on the existing templa…
  preprints 2022 置信度 0.74
• ROPIUS0: A deep learning-based protocol for protein structure prediction and model selection and its performance in CASP14
  Protein structure prediction has recently been revolutionized when AlphaFold2 [1] predicted protein structures with near-experimental accuracy in the latest CASP14 season of critical assessment of methods of protein structure prediction (CASP). Among numerous …
  preprints 2021 置信度 0.74
• MULTICOM2: an open-source protein structure prediction system powered by deep learning and distance prediction
  Protein structure prediction is an important problem in bioinformatics and has been studied for decades. However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we present our…
  preprints 2021 置信度 0.74
• MMpred: a distance-assisted multimodal conformation sampling for de novo protein structure prediction
  Motivation The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal …
  preprints 2021 置信度 0.74
• Improved protein structure prediction by deep learning irrespective of co-evolution information
  We describe our latest study of the deep convolutional residual neural networks (ResNet) for protein structure prediction, including deeper and wider ResNets, the efficacy of different input features, and improved 3D model building methods. Our ResNet can pred…
  preprints 2020 置信度 0.74
• Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins
  Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologues of the target protein. The most successful approaches combine large feature sets derived from MSAs, and considerable co…
  preprints 2020 置信度 0.74
• ProALIGN: Directly learning alignments for protein structure prediction via exploiting context-specific alignment motifs
  Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under prediction and th…
  preprints 2020 置信度 0.74
• Improved protein structure prediction using predicted inter-residue orientations
  The prediction of inter-residue contacts and distances from co-evolutionary data using deep learning has considerably advanced protein structure prediction. Here we build on these advances by developing a deep residual network for predicting inter-residue orie…
  preprints 2019 置信度 0.74
• CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction
  Protein functions are largely determined by the final details of their tertiary structures, and the structures could be accurately reconstructed based on inter-residue distances. Residue co-evolution has become the primary principle for estimating inter-residu…
  preprints 2020 置信度 0.74
• Protein structure prediction and design in a biologically-realistic implicit membrane
  ABSTRACT Protein design is a powerful tool for elucidating mechanisms of function and engineering new therapeutics and nanotechnologies. While soluble protein design has advanced, membrane protein design remains challenging due to difficulties in modeling the …
  preprints 2019 置信度 0.74
• A <i>de novo</i> protein structure prediction by iterative partition sampling, topology adjustment, and residue-level distance deviation optimization
  Motivation With the great progress of deep learning-based inter-residue contact/distance prediction, the discrete space formed by fragment assembly cannot satisfy the distance constraint well. Thus, the optimal solution of the continuous space may not be achie…
  preprints 2021 置信度 0.74
• Analysis of distance-based protein structure prediction by deep learning in CASP13
  This paper reports the CASP13 results of distance-based contact prediction, threading and folding methods implemented in three RaptorX servers, which are built upon the powerful deep convolutional residual neural network (ResNet) method initiated by us for con…
  preprints 2019 置信度 0.74
• Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13
  Computational methods that produce accurate protein structure models from limited experimental data, e.g. from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a…
  preprints 2019 置信度 0.74
• Sequence alignment using machine learning for accurate template-based protein structure prediction
  Motivation Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high se…
  preprints 2019 置信度 0.74
• Prot-LAMBDA: Explicit Distance Learning Enhances Structural Reasoning in Protein Language Models
  Protein language models (PLMs) learn evolutionary information from large-scale sequence data, but three-dimensional relationships are encoded only implicitly. Here, we introduce Prot-LAMBDA (Protein LAnguage Model Boosted with Distance Awareness), a PLM that e…
  preprints 2026 置信度 0.74
• WaterFlow: Prediction of Ordered Water Molecule Positions on Protein Structures
  Ordered water molecules mediate many protein functions including stability, ligand binding, and catalysis. Predicting their positions with sub-angstrom accuracy would support protein design, binding affinity prediction, and automated model building in X-ray cr…
  preprints 2026 置信度 0.74
• Large-scale structure prediction of DUF-containing protein-protein interactions
  Motivation Continuing advances in genome and metagenome sequencing expand the number of identified conserved protein families that remain functionally uncharacterized and contain domains of unknown function (DUFs). Functional-association resources such as STRI…
  preprints 2026 置信度 0.74
• HDOCK-Multimer: integrating docking and combinatorial assembly for structure prediction of large protein complexes
  Deep learning methods, such as AlphaFold and RosettaFold, achieve high accuracy in protein structure prediction. However, predicting the structure of large protein complexes remains challenging due to their large size and intricate multi-chain interactions. Do…
  preprints 2026 置信度 0.74
• Mavchen 1: A Conformational Ensemble Platform for Protein Ligand Pose Prediction That Substantially Outperforms Static Structure Prediction in a Category-Stratified Benchmark
  Deep learning structure predictors, most prominently AlphaFold2 (the field-standard tool benchmarked against throughout this study), have substantially expanded access to protein structural information, yet characteristically return a single static conformatio…
  preprints 2026 置信度 0.74
• AlphaConformers: Structure-guided sampling enables prediction of multiple protein conformations
  Proteins are dynamic molecules capable of adopting multiple conformations. However, AlphaFold2 predominantly generates models around a single conformation, usually representing a ligand-bound state. To address this limitation, we developed AlphaConformers, a s…
  preprints 2026 置信度 0.74
• AntiSite: Modality Dropout Enables Antibody Paratope Prediction With or Without Structure From a Single Model
  Summary Reliable paratope identification is central to understanding antibody–antigen recognition and advancing therapeutic antibody discovery. AntiSite is a unified antibody paratope prediction framework that combines protein-language-model sequence embedding…
  preprints 2026 置信度 0.74
• ProtBLIP2-SST: Protein Function Prediction via BLIP2 with Sequence, Structure, and Text
  Protein function prediction traditionally relies on structured gene ontology (GO) labels or multi-label classifiers. However, these labels or classifiers cannot flexibly describe molecular function, biological process, cellular component, and free-text functio…
  preprints 2026 置信度 0.74
• Beyond Chemical Similarity: Structure-Agnostic Drug–Drug Interaction Prediction with MeSH Semantics and a Drug–Target–Protein Knowledge Graph
  Background Adverse drug–drug interactions (DDIs) cause preventable hospitalizations, but exhaustive experimental screening of all drug pairs is infeasible. Many computational predictors rely on SMILES or other molecular representations, limiting their direct a…
  preprints 2026 置信度 0.74
• A discrete protein subset drives structure prediction discordance in orphan proteins
  Structure and disorder predictors are increasingly used as decision-grade tools in protein engineering and in the analysis of newly emerged proteins, yet how the current state-of-the-art behaves on sequences outside the well-charted evolutionary space remains …
  preprints 2026 置信度 0.74
• ppigFinder: an integrated desktop application for bacterial genome annotation and AlphaFold 3-based protein–protein interaction screening
  Motivation AlphaFold-based structure prediction has transformed structural biology by enabling accurate protein modelling and providing a powerful framework for inferring protein-protein interactions (PPIs). However, discovering candidate PPIs directly from ge…
  preprints 2026 置信度 0.74
• De <i>novo</i> protein structure prediction using ultra-fast molecular dynamics simulation
  ABSTRACT Modern genomics sequencing techniques have provided a massive amount of protein sequences, but experimental endeavor in determining protein structures is largely lagging far behind the vast and unexplored sequences. Apparently, computational biology i…
  preprints 2018 置信度 0.74
• EpiTune: An Accurate Epitope Prediction Model with Mechanistic Insights
  Summary The accurate prediction of b-cell epitopes facilitates vaccine development by identifying known antibodies for an antigen. Multiple epitope prediction models use protein language models to enable more accurate predictions with modest results. Here, we …
  preprints 2026 置信度 0.74
• Structure-verified graph attention networks for residue-level prediction of antibody-contact escape mutations in the SARS-CoV-2 spike protein
  Abstract Predicting which SARS-CoV-2 spike mutations enable antibody escape requires training labels whose biological relevance can be independently verified. We built a two-stage pipeline linking 905 Indian SARS-CoV-2 genome sequences to solved antibody-spike…
  preprints 2026 置信度 0.74
• INDELVAR: structure-informed prediction of in-frame indel pathogenicity with calibrated PP3/BP4 thresholds
  In-frame insertions and deletions are difficult to interpret because their effects depend on both the sequence change and its protein context. We developed INDELVAR, a random forest model for in-frame insertions and deletions of 1-10 amino acids that integrate…
  preprints 2026 置信度 0.74
• Deep learning-guided identification of bacteriophage receptor-binding protein candidates for foodborne pathogen detection
  Foodborne pathogens including Salmonella spp., Escherichia coli and Listeria monocytogenes cause an estimated 600 million illnesses annually. Yet conventional detection methods remain slow, costly, or insufficiently specific for routine food safety surveillanc…
  preprints 2026 置信度 0.74
• Pi-Ensemble: Sequence-guided generation of interpolated protein conformational ensembles
  Proteins are critical biomolecular machines that populate ensembles of interconverting conformations. Many biological processes depend on transitions between metastable states. Although molecular dynamics (MD) simulations provide a physically grounded route to…
  preprints 2026 置信度 0.74
• PathFold: Predicting the Entire Protein Folding Pathway from Protein Sequence Alone
  Recent advances in protein structure prediction, exemplified by AlphaFold, have largely addressed the determination of static structures, one aspect of the protein folding problem. However, predicting folding pathways, by which proteins reach their native stat…
  preprints 2026 置信度 0.74
• Accurate <i> ΔT _m </i> Prediction Without Protein Structure Inputs for Biomolecular Stability
  Predicting protein stability, like changes in melting temperature ( ΔT m ) caused by mutations, is a critical task in therapeutic protein engineering and drug discovery. This is reflected by a growing solution space, including both AI-based sequence and struct…
  preprints 2026 置信度 0.74
• A generalisable framework to inject distance information into Alphafold-like structure predictors
  Structure prediction methods are now highly successful at predicting three-dimensional structures from sequence. However, it is still often desirable to supplement these methods with additional external priors on pairwise distances in the structures. We presen…
  preprints 2026 置信度 0.74
• A novel benchmark dataset for enzyme function prediction reveals the limitations of state-of-the-art models
  Accurate computational prediction of enzyme function, standardized by Enzyme Commission (EC) numbers, is essential for large-scale genome annotation and generative enzyme design. However, it remains unclear whether state-of-the-art predictors learn the intrins…
  preprints 2026 置信度 0.74
• A Glycan-Aware Diffusion Model for Carbohydrate and Glycoprotein Structure Prediction
  Biomolecular diffusion models can now predict proteins and heterogeneous complexes, but glycans remain difficult because their branched topology, conformational flexibility, and strict stereochemical rules must be captured simultaneously. We developed SweetFol…
  preprints 2026 置信度 0.74
• ConfDock: Atom-specific Uncertainty Quantification for Molecular Docking via Conformal Prediction
  Molecular docking is widely used in structure-based drug discovery, yet most approaches provide point estimates without rigorous uncertainty quantification. This limitation makes it difficult to assess when a predicted pose should be trusted, especially when d…
  preprints 2026 置信度 0.74
• Modelling antibody structures at the speed of language
  Protein structure prediction is currently substantially slower than obtaining sequence representations of proteins. This leads to most property prediction methods relying solely on trivial or learned sequence embeddings. However, contemporary structure predict…
  preprints 2026 置信度 0.74
• Prot2Prop: Structure-informed multitask protein property prediction
  Protein engineering often relies on separate models for related developability properties, limiting efficiency and transfer across tasks. We present Prot2Prop, a multitask framework based on a frozen ProstT5 encoder with shared and task-specific adapters for j…
  preprints 2026 置信度 0.74
• HInt: interaction-based homology discovery through accelerated genome-scale AlphaFold screening
  ABSTRACT Identifying homologous proteins across deep evolutionary distances remains a major challenge because sequence and structural similarity progressively become undetectable over time. Although protein-protein interactions (PPIs) are often constrained by …
  preprints 2026 置信度 0.74
• Multi-Peptide Prompting Enables In-Context Learning in Protein Language Models
  A bstract Protein language models (PLMs) are trained primarily on individual protein sequences, yet many peptide-discovery problems require inference from only a small number of labeled examples. Here, we show that single-sequence PLMs can perform in-context p…
  preprints 2026 置信度 0.74
• PINPOINT: Protease INhibitor PredictiOn at the plant–pathogen INTerface using protein language models and structural modeling
  Summary Cysteine and serine proteases act as an immune hub in the plant apoplast to provide robust extracellular immunity during microbial colonisation. Microbial pathogens counteract these immune proteases by inhibiting their activity using small secreted pro…
  preprints 2026 置信度 0.74
• ProtPen combines sequence- and structure-based approaches to facilitate protein function predictions on a proteome-wide scale
  Proteins of unknown function represent a significant gap in our understanding of biological processes, encompassing large portions of the proteomes of many organisms, especially prokaryotes. Addressing this gap is critical to understanding the biology and path…
  preprints 2026 置信度 0.74
• MHChron: diversity-balanced dataset design for robust peptide–MHC binding prediction across MHC class I and II
  Accurate prediction of peptide–MHC (pMHC) binding is central to immunogenicity assessment, yet many existing predictors are trained and evaluated on narrow allele sets and restricted peptide-lengths. Here, we present MHChron, a unified pMHC binding prediction …
  preprints 2026 置信度 0.74
• ProtEnrich: Residual Multimodal Enrichment of Protein Sequence Embeddings
  Protein language models effectively capture evolutionary and functional signals from sequence data but lack explicit representation of the biophysical properties that govern protein structure and dynamics. Existing multimodal approaches attempt to integrate su…
  preprints 2026 置信度 0.74
• OmniScore: Universal Scoring of Diverse Biomolecular Complexes via Equivariant Geometry-Aware Discrete Representation Learning
  A bstract Scoring biomolecular complexes is central to structure assessment and drug discovery, yet the complexes themselves vary widely in pose, size, and molecular composition. A scoring function tuned for one interaction type rarely carries over to another,…
  preprints 2026 置信度 0.74
• EpiESM-GA: Resource-Efficient Protein Foundation Model Features for Equitable B-Cell Epitope Prediction
  Prediction of B-cell epitopes can assist in reducing costly wet-lab screening in vaccine design, diagnostics, and antibody discovery. However, current predictors often suffer from noisy labels, weak generalization, and structure-dependent workflows. Here we pr…
  preprints 2026 置信度 0.74
• Evaluating Graph Neural Network Architectures for Multi-Omics Cancer Subtyping using Methylation and Gene Expression Profiles
  Motivation Graph Neural Networks (GNNs) have gained increasing interest in the biomedical domain, as the integration of prior knowledge and deep neural networks has the potential to enhance insights into molecular processes and disease mechanisms. However, a c…
  preprints 2026 置信度 0.74
• Benchmarking confidence estimation and rescoring for cyclic peptide–protein complex predictions
  Cyclic peptides are a rapidly expanding class of therapeutics, but the reliability of deep-learning structure prediction for cyclic peptide–protein complexes has not been systematically evaluated. We assembled a curated benchmark of 111 non-redundant complexes…
  preprints 2026 置信度 0.74
• LLM-H2G: biomedical semantic-enhanced hypergraph contrastive learning for herb--disease association prediction
  Abstract Background : Herb–disease association prediction is central to computational traditional medicine, but existing graph and hypergraph methods mainly rely on observed topology and underuse biomedical textual semantics, especially in heterogeneous or spa…
  preprints 2026 置信度 0.74
• Benchmarking Deep Learning Predictions of Mutation-Induced Fold Switching
  Many proteins are known to adopt multiple distinct folded states which are often associated with key functional behavior. A predictive understanding of the properties of such fold-switching or metamorphic proteins can provide insights into protein dynamics and…
  preprints 2026 置信度 0.74
• SQUARNA: stem maximization for accurate de novo RNA secondary structure prediction
  ABSTRACT Non-coding RNAs play diverse roles in a wide range of cellular processes, with their spatial structure being pivotal to their function. RNA secondary structure is a key determinant of its overall fold. Given the scarcity of experimentally determined R…
  preprints 2026 置信度 0.74
• XL-MS–Guided Structure Prediction of Disordered <i>Encephalitozoon hellem</i> Proteins
  Microsporidia such as Encephalitozoon hellem are obligate intracellular human parasites that remain genetically intractable, limiting functional characterization of their proteomes. Structural studies based on homology-based modeling and the use of deep learni…
  preprints 2026 置信度 0.74
• Proteome-Scale Mining and Multi-Objective Prioritization of Encrypted Antimicrobial Peptides with Experimental Validation
  Encrypted antimicrobial peptides (eAMPs) are bioactive fragments embedded within larger proteins and represent an underexplored source of antimicrobial candidates. We developed a multi-layer proteome-mining framework to identify and prioritise eAMPs from 95%-i…
  preprints 2026 置信度 0.74
• One of the Above, None of the Above: Why the Same Model Should Not Predict Both
  Abstract Novelty is often modeled as the probability that the next observation opens a new category. However, experimental setups often leave implicit the fact that novelty judgements are performed within a context that makes such judgements meaningful.This pa…
  preprints 2026 置信度 0.74
• ProtAug: An Empirical Investigation of pLM-Guided Data Augmentation for Protein Sequence Prediction Tasks
  Protein language models (pLMs) offer great potential for protein sequence analysis, yet the scarcity of labeled data often limits their effectiveness in fine-tuning. Data augmentation is a promising remedy, but systematic evaluation of augmentation strategies …
  preprints 2026 置信度 0.74
• Capabilities, specificity gaps and training-data dependence of AlphaFold3 across diverse application areas
  Structure prediction models have moved from single proteins to assemblies that include diverse biomolecules and their modifications. AlphaFold3 (AF3) and related models extended structural modelling via an all-atom framework, opening many new potential applica…
  preprints 2026 置信度 0.74
• Folding the unfoldable 2: using AlphaFold and ESMFold to explore spurious proteins
  Motivation Spurious protein sequences, resulting from gene prediction errors, theoretically should not yield folded structures. AlphaFold2 was previously shown to predict short spurious sequences with high pLDDT scores and was therefore unlikely to distinguish…
  preprints 2026 置信度 0.74
• Bioinf-Farma: supervised integration of epitope prediction and recombinant protein developability for automated vaccine candidate prioritization
  Vaccine antigen discovery requires prioritizing protein candidates according to both immunogenic potential and recombinant expression feasibility. These properties are typically evaluated using separate computational tools, requiring researchers to integrate h…
  preprints 2026 置信度 0.74
• VelocityFM: Short-Horizon Protein Trajectory Prediction via Flow Matching in Velocity Space
  Protein dynamics is fundamentally a trajectory prediction problem, but molecular dynamics (MD) simulation remains expensive and static structure predictors do not model time-ordered motion. We present VelocityFM , a short-horizon protein trajectory predictor t…
  preprints 2026 置信度 0.74
• ProtAff: Protein Binding Affinity Prediction via LoRA-Finetuned ESM-2
  Predicting the binding affinity of protein–protein interactions remains a central challenge in computational biology. Structure prediction models such as AlphaFold3 (AF3) and Boltz-2 can produce high-quality docking poses, and their confidence scores indicate …
  preprints 2026 置信度 0.74
• Whole-proteome structure/function prediction in Uropathogenic <i>E. coli</i> reveals previously missed host-microbe and microbe-phage interaction pathways
  ABSTRACT The rapid advancement of high-throughput sequencing technologies has vastly increased the number of known protein sequences, but the experimental characterization of their structures and functions lags behind. This gap in knowledge impedes our underst…
  preprints 2026 置信度 0.74
• Automated dataset integrating structural and bioactivity data for structure-based drug discovery
  Abstract Machine learning for structure-based drug design needs large datasets linking protein-ligand structures to quantitative bioactivity values. DockTData is an openly licensed structural bioactivity dataset that links experimentally determined protein-lig…
  preprints 2026 置信度 0.74
• AFilter: Improved Antibody Epitope Prediction by Machine Learning-Optimized Interface Energy Filtering of AlphaFold3-Predicted Complex
  AlphaFold3 (AF3) predicts protein-complex structures from sequence with near-experimental accuracy on many targets, substantially lowering the cost of mechanistic and therapeutic discovery. However, application to antibody epitope prediction is hampered by an …
  preprints 2026 置信度 0.74
• Peptide:MHC Binding Stability Prediction Using Protein Language Models
  A bstract Peptide:MHC class I (pMHC-I) binding stability governs the persistence of antigenic complexes at the cell surface and plays a key role in facilitating downstream immunological signals such as antigen presentation, T-cell activation, and immunodominan…
  preprints 2026 置信度 0.74
• An Evaluation Framework of Biomolecular Energetics Learned by Predictive Models
  Abstract Deep learning has revolutionized protein structure prediction, with function prediction on the horizon. Biomolecular properties, including structure and all aspects of function, emerge from atomic-level interactions and the probabilities of their form…
  preprints 2026 置信度 0.74
• GNMCADS: Sampling For Protein Conformation Diversity With Gaussian Network Model Guided Condition Annealed Diffusion Sampler
  Proteins are dynamic molecules existing in diverse conformational states underlying their biological functions. Although recent approaches have enabled diverse conformational sampling by emulating molecular dynamics simulations, perturbing evolutionary informa…
  preprints 2026 置信度 0.74
• MDFitML: Using machine learning to predict potency from molecular simulations
  Abstract Molecular dynamics (MD) simulations characterize ligand-protein conformational dynamics and can provide a strong structural rationale for measured potency changes accompanying ligand modifications. We present an automated workflow that streamlines not…
  preprints 2026 置信度 0.74
• Predicting the Stability of Biologics from Protein Structure and Kinetics
  Abstract Predicting the stability of biologics remains a critical challenge due to the multiple degradation pathways involved in the kinetics of biologics and the inherent complex relationship between protein structure and dynamics. A strict validation process…
  preprints 2026 置信度 0.74
• DDI_single: Single-Sequence-Based Protein Domain Assembly
  Domains are the basic units of protein structure and function. Appropriate inter-domain organization is critical to enable cooperative execution of multiple related functions. It is thus a crucial step to determine the full-length structure of multi-domain pro…
  preprints 2026 置信度 0.74
• SAPPTree: Identification of an S-Acylation Motif Drives a Novel S-Acylation Prediction Program
  S-acylation, the reversible addition of fatty acids to proteins, has emerged as an abundant post-translational modification that drives protein localization and function. With no known consensus sequence, current prediction programs rely on machine learning al…
  preprints 2026 置信度 0.74
• Bayesian-Steered Structure Prediction of Mechanical Biomolecules Using Twisted Diffusion
  Deep learning approaches have revolutionized protein structure prediction. These tools are trained using experimental data and recapitulate reported conformations, but there is great interest in predicting conformations that may be functionally relevant althou…
  preprints 2026 置信度 0.74
• Cancer Cell Line Heterogeneity Imposes a Primary Bottleneck for Virtual Perturbation Screening at Scale
  UniPert-G2CP (Li et al., Cell, 2026) bridges genetic and chemical screens from molecular representation to phenotype modeling across five cancer cell lines [1]. Here we extend this architecture to 162 cell lines, 32,039 compounds, and genome-wide output (12,32…
  preprints 2026 置信度 0.74
• Handshake: Partner-Specific Protein-Protein Binding Site Prediction at Scale Using ProstT5 and Cross-Chain Attention
  Partner-specific protein-protein binding site prediction, identifying which residues of a protein form the interface when bound to a specific partner, remains a challenging task with significant implications for drug discovery and understanding of protein stru…
  preprints 2026 置信度 0.74
• Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?
  Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a re…
  preprints 2018 置信度 0.74
• Structure-free, site-resolved contrastive learning extends small-molecule discovery beyond the reach of structure-based modeling
  Virtual screening asks which molecules, among an enormous space of drug-like chemistry, are worth synthesizing and testing against a protein target. Most modern methods answer this question by building and scoring an explicit three-dimensional pose through mol…
  preprints 2026 置信度 0.74
• GeoEPred: A Multimodal Structure-Aware Geometric Deep Learning Framework for Gram-Negative Bacterial Secreted Effector Prediction with Sequence Semantics
  Accurate prediction of effector proteins secreted by Gram-negative bacteria is important for elucidating bacterial pathogenic mechanisms and developing precise anti-infective strategies. Although existing methods have benefited from the strong sequence feature…
  preprints 2026 置信度 0.74
• Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins
  Protein structure predictors achieve high single-state accuracy, but it remains unclear whether they can recover functionally relevant conformational ensembles or account for the presence of ligands and/or binding partners. Here, we benchmark AlphaFold3, Boltz…
  preprints 2026 置信度 0.74
• A geometric atlas of how ESM3 organizes modalities across depth
  Protein language models learn general-purpose representations from large collections of protein sequences and structures, and have advanced the prediction of protein structure and function. ESM3 is a multimodal protein language model that ingests a protein thr…
  preprints 2026 置信度 0.74
• Structure-Guided Prioritization of Amphipathic Antimicrobial Peptide Candidates against ESKAPE Pathogens via Generative Design and Physicochemical Filtering
  Abstract Antimicrobial resistance (AMR) among ESKAPE pathogens poses an escalating threat to global health as conventional antibiotic pipelines fail to keep pace with emerging resistance. Antimicrobial peptides (AMPs) have re-emerged as a promising alternative…
  preprints 2026 置信度 0.74
• FuncSeek: Multi-PLM contrastive learning for protein functional similarity search
  Below 30% pairwise sequence identity, alignment-based methods struggle to reliably distinguish true homologs from chance, and enzyme function prediction degrades accordingly: on proteins in this regime, even advanced methods (CLEAN) achieves only 55.1% accurac…
  preprints 2026 置信度 0.74
• ProMiSE: Protein Multi-State Evaluation Benchmark in Biological Contexts
  Proteins are inherently dynamic, with biological functions often emerging from transitions between multiple conformational states. While recent breakthroughs have largely addressed the static structure prediction problem, no systematic benchmark exists to demo…
  preprints 2026 置信度 0.74
• Comparison of AI protein structure ensemble prediction tools
  Multiple AI prediction tools for protein structural ensembles have recently been released, building on the much heralded advances from AlphaFold, large language models, and other machine-learning approaches. Here we report on a comparison of several tools (Bio…
  preprints 2026 置信度 0.74
• Decoding antibiotic modes of action from multimodal cellular responses
  Abstract Antibiotic resistance continues to rise, yet most new drug candidates act through long-established targets. Faster mode of action (MoA) assessment would enable more effective prioritization of screening hits and help identify compounds with novel mech…
  preprints 2026 置信度 0.74
• A Chromatin Biology Assessment of AlphaFold3
  Biomolecular structure prediction tools such as AlphaFold have achieved remarkable success in predicting structures of single proteins and multiprotein complexes. AlphaFold3 now incorporates the capability to model complexes containing nucleic acids and chemic…
  preprints 2026 置信度 0.74
• ThermoFusion: A Multimodal Deep Learning Framework for Generalizable Prediction of Enzyme Thermostability
  Protein thermostability is a critical property for both industrial and biomedical enzyme applications, yet experimental evaluation of mutation-induced stability changes remains laborious and costly. Here, we present ThermoFusion, a hybrid deep learning framewo…
  preprints 2026 置信度 0.74
• Application of vision transformers to protein-ligand affinity prediction
  Abstract Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D…
  preprints 2026 置信度 0.74
• Ancient Reconstructed Proteins: A Framework for Resurrecting Protein Structures from Million-Year-Old Metagenomes
  Abstract DNA sequences derived from ancient samples provide insights into human history, paleoenvironments, and evolutionary biology. Advances in laboratory techniques and computational tools have established ancient DNA research as a distinct field. However, …
  preprints 2026 置信度 0.74
• Benchmarking MSA pairing for protein-protein complex structure prediction reveals a depth-over-pairing principle
  ABSTRACT AlphaFold-Multimer (AFM) and AlphaFold3 (AF3) have revolutionized protein-protein complex structure prediction. However, it remains unclear whether elaborate MSA pairing is a strict prerequisite for achieving high accuracy. Here, we systematically eva…
  preprints 2026 置信度 0.74
• Just Add Structure: Protein Language Models Combined with Structural Equivariance Excel at Protein Tasks
  Accurate in silico prediction of protein properties, functional fitness, and mutational effects remains a central challenge in protein engineering and therapeutic design. While Protein Language Models (PLMs) successfully capture rich evolutionary and functiona…
  preprints 2026 置信度 0.74
• Drug Design Studio (DDS) 2.0: A Unified Platform for Network Pharmacology Integrated with Docking and Virtual Screening Workflow for Covalent/Non-Covalent Binders
  Network pharmacology has become a central paradigm in modern drug discovery, replacing the reductionist "one drug, one target" view with a systems-level understanding of how compounds engage networks of proteins that are linked to disease. Despite its impact, …
  preprints 2026 置信度 0.74
• Zero-Shot Generation of Protein Conformational Ensembles Through AlphaFold Latent Flooding
  Abstract Despite transformative advances in protein structure prediction, generating conformational ensembles directly from sequence in an efficient and accessible manner remains a central challenge. We introduce a heuristic importance sampling framework for t…
  preprints 2026 置信度 0.74
• Promera: a unified model for biomolecular structure prediction, filtering, and design
  Generative models have become staple tools for modeling and designing biomolecular structures. However, although these tools have improved in structural prediction accuracy, their ability to filter designed binders—an essential use case—remains insufficient; w…
  preprints 2026 置信度 0.74
• EditorForge: An Active-Site-Aware Framework for Inverse-Folding-Based Protein Redesign
  Inverse-folding models can rapidly generate protein sequences compatible with a supplied backbone, but unconstrained redesign is poorly suited to enzyme and genome-editor-associated domains, where catalytic, substrate-proximal, and conserved structural regions…
  preprints 2026 置信度 0.74
• Assessing phylogenetic inference in the era of AI-driven protein structures
  Phylogenetic inference is one of the most common downstream applications of multiple sequence alignment. Quantitative assessment of inferred trees is essential for evaluating the reliability of both alignments and phylogenetic inferences. Establishing a robust…
  preprints 2026 置信度 0.74
• Mapping pathogenic patterns in membrane transporters from the GLUT transporter family
  Non-synonymous amino acid substitutions (missense mutations) are common in the general population; some are causative of serious disease. Depending on their structural context, they can disrupt protein function, folding, or dynamics. Computational predictive m…
  preprints 2026 置信度 0.74
• Scaling and democratising structure-based protein function prediction with metagenomic-deepFRI
  High-quality protein structure models have become widely available, offering insights into protein function, yet they remain underutilized. Here, we introduce metagenomic-deepFRI, a framework incorporating structural templates into functional annotation pipeli…
  preprints 2026 置信度 0.74
• ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures
  Deep learning-based protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet reliably assessing the quality of computationally predicted multimeric structures remains challenging. Recent methods have demonstrat…
  preprints 2026 置信度 0.74
• makeshift: a lightweight software for accessing and analyzing NMR data and protein dynamics
  Nuclear magnetic resonance (NMR) spectroscopy yields rich residue-level information on biomolecular dynamics and chemical environments, two frontiers for quantitative predictive methods in biochemistry. Decades of data are publicly archived in the Biological M…
  preprints 2026 置信度 0.74
• Artificial intelligence aided design of peptides with custom secondary structure motifs and reduced amino acid alphabets
  Proteins are highly diverse functional polymers where the specific sequence of amino acids, selected from a standard genetically-encoded alphabet of twenty (C20), determines the structure and ultimately the function of the resulting folded protein. This standa…
  preprints 2026 置信度 0.74
• Earthquake aftershock forecasting with conditional generative models
  Abstract Forecasting how aftershocks evolve in space and time after a large earthquake is a central problem in statistical seismology and underpins operational earthquake forecasting. Existing forecasting methods rest on statistical point-process models such a…
  preprints 2026 置信度 0.74
• Improving Biological Sequence Prediction with AlphaFold2 Representation
  Motivation Accurate prediction of functional sites from primary sequences is essential for elucidating biological mechanisms and advancing rational drug design. However, traditional sequence-based features are inherently unable to capture complex structural pr…
  preprints 2026 置信度 0.74
• What makes a banana false? How the genome of Ethiopian orphan staple Ensete ventricosum differs from the banana A and B sub-genomes
  Abstract Background Ensete ventricosum , also known as the “tree against hunger” plays a key role in Ethiopian food security and farming systems, feeding more than 20 million people. Since domestication via clonal selection in the south-west Ethiopian highland…
  preprints 2026 置信度 0.74
• Overestimating zero-shot fitness prediction: Broad benchmarks mask local failures and practical limitations
  Deep learning models have emerged as promising tools in protein engineering. In particular, they can be used to predict mutation fitness without the need for task-specific training, a process known as zero-shot prediction. However, the respective strengths and…
  preprints 2026 置信度 0.74
• Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites
  Allosteric regulation represents a fundamental mechanism of protein function, yet distinguishing allosteric from orthosteric protein binding sites remains a persistent computational challenge. While multimodal protein foundation models offer the potential to i…
  preprints 2026 置信度 0.74
• Stoic: Fast and accurate protein stoichiometry prediction
  Motivation Protein complexes are central to cellular function, but experimental determination of their structures remains challenging. Structure prediction methods require prior knowledge of stoichiometry - the number of copies of each protein entity within a …
  preprints 2026 置信度 0.74
• A Linguistic Model for Protein Structure Generation
  Computational generation of protein backbone geometry is a critical upstream step in de novo protein design, but the existing state of the art approaches typically requires large pretrained structure prediction networks, significant computing in frastructure, …
  preprints 2026 置信度 0.74
• Improving All-Atom Molecular Dynamics Models for Quantitative Prediction of Nanopore Blockade Current
  All-atom molecular dynamics has become an indispensable tool in development of nanopore sensors of biological information. In a typical nanopore experiment, measurements of ionic current flowing through a nanopore report on the chemical structure of biomolecul…
  preprints 2026 置信度 0.74
• Systematic Evaluation of AlphaFold2 and OpenFold3 on Protein–Peptide Complexes
  Protein–peptide interactions are important mediators of diverse biological processes. While deep learning has revolutionized protein structure prediction, comparative evaluation of these methods, specifically for protein–peptide complexes, remains an area of a…
  preprints 2026 置信度 0.74
• Scalable Extraction of Information on Protein-Protein Interactions using Topological Data Analysis
  Protein-protein interactions (PPIs) govern a wide range of cellular functions. The ability to predict PPI interfaces from protein molecular surfaces is important for understanding protein function and enabling therapeutic discovery. While recent advances in st…
  preprints 2026 置信度 0.74
• SPINDLE: Unlocking protein dynamics from single-field NMR relaxation data using a deep learning ensemble
  A protein’s function is derived from its three-dimensional structure and the motions of the atoms about that structure. The detailed characterization of both macromolecular structure and dynamics provides an opportunity for understanding enzyme catalysis, liga…
  preprints 2026 置信度 0.74
• ProtJEPA: A Multimodal Joint-Embedding Predictive Architecture for Protein Biological World Modeling with Multi-Teacher Modality-Attentive Fusion
  Over 99.9% of known protein sequences lack experimentally validated functional annotations. We present ProtJEPA, a multimodal Joint-Embedding Predictive Architecture that trains a sequence-only student encoder to predict joint embeddings spanning ten biologica…
  preprints 2026 置信度 0.74
• Biological Network Organization, Not Generic Graph Topology, Drives Graph-Based Gene Essentiality Prediction
  Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulnerabilities. Graph neural networks (GNNs) integrate molecular interaction networks with gene-level features, but…
  preprints 2026 置信度 0.74
• A New Enzyme Family Catalyzing Methyllanthionine Sulfoxide Formation in Anti-phage Lanthipeptides
  Lanthipeptides are one of the largest classes of ribosomally synthesized and post-translationally modified peptides (RiPPs). The coi biosynthetic gene cluster (BGC) from Streptomyces coelicolor A3(2) encodes a canonical class I lanthipeptide dehydratase (CoiB)…
  preprints 2026 置信度 0.74
• MAXWELL: Calibrating the probabilistic outputs of protein language models to the mutation-induced stability change landscape
  Designing mutations that enhance protein stability is a central goal in protein engineering. However, experimentally screening large numbers of candidate mutations is costly and time-consuming, creating a strong need for computational methods that can identify…
  preprints 2026 置信度 0.74
• CryoARC: Atomic-resolution conformational landscapes of protein assemblies from cryo-EM single particles with evolutionary priors
  Single-particle cryo-electron microscopy (cryo-EM) reveals structural heterogeneity in macromolecular complexes, but recovering continuous conformational landscapes at high resolution remains challenging. Here, we introduce CryoARC, a deep learning framework t…
  preprints 2026 置信度 0.74
• BoltzProt-1: Towards Efficient De Novo Binder Design with Good Developability
  Designing binders against novel protein targets remains a central challenge in computational drug discovery. Here we introduce BoltzProt-1 , a pipeline for generating protein binders, including nanobodies, with improved hit rates and favorable developability p…
  preprints 2026 置信度 0.74
• Computational prediction resolves thousands of homooligomeric phage protein structures
  Bacteriophages (phages) play essential roles in microbial systems, yet most phage proteins remain poorly characterised. Protein tertiary and quaternary structure information contributes valuable information about protein function. As many phage proteins functi…
  preprints 2026 置信度 0.74
• G-SPRI: A Structure-Centric Graph Model for Comprehensive Prediction of Cancer Driver Events from Missense Mutations
  ABSTRACT In silico approaches for predicting the functional impact of missense mutations are critical for interpreting personal genomes and identifying disease-related biomarkers. Existing methods largely rely on sequence-based information or intuitive structu…
  preprints 2026 置信度 0.74
• Graph Machine Learning for Physiological Role Prediction in Protein Contact Networks: A Large-Scale Comparative Study on the Human Proteome
  Proteins are fundamental macromolecules involved in virtually all biological processes. Their physiological roles are tightly linked to their three-dimensional structure, which can be naturally abstracted as Protein Contact Networks (PCNs), i.e., graphs where …
  preprints 2026 置信度 0.74
• A structure-derived contact-network responsiveness atlas of human proteins
  Protein structures encode non-local contact organization, but static coordinates do not directly quantify how a contact network responds when effective stabilizing interactions are strengthened or weakened. Here we introduce Contact-Network Responsiveness (CNR…
  preprints 2026 置信度 0.74
• Integrating AI and molecular modeling for structural prediction of a closed state of the hERG channel
  The voltage-gated potassium channel hERG (Kv11.1) plays a central role in cardiac repolarisation by mediating the rapid delayed rectifier K⁺ current (IKr). Blockage of hERG by small molecules can lead to delayed repolarisation, QT interval prolongation, and po…
  preprints 2026 置信度 0.74
• Antibody–Antigen Affinity Prediction with Chain-Aware Protein Language Modeling
  Motivation Antibody-antigen affinity determines which antibodies advance in therapeutic discovery, repertoire analysis and affinity maturation, but experimental measurements are sparse relative to the scale of sequence libraries. Structure-based predictors can…
  preprints 2026 置信度 0.74
• Drug Design Studio (DDS) 2.0: A Unified Platform for Network Pharmacology Integrated with Docking and Virtual Screening Workflow for Covalent/Non-Covalent Binders
  Network pharmacology has become a central paradigm in modern drug discovery, replacing the reductionist "one drug, one target" view with a systems-level understanding of how compounds engage networks of proteins that are linked to disease. Despite its impact, …
  preprints 2026 置信度 0.74
• Rv0810c: a genus-conserved, structurally ordered small protein of unknown function carrying DUF3073 in Mycobacterium tuberculosis
  Small annotated open reading frames are the most neglected part of the functionally un-characterised Mycobacterium tuberculosis genome. We revisit Rv0810c, a 60-residue protein carrying the unknown-function domain DUF3073 (Pfam PF11273), flagged as an Actinoba…
  preprints 2026 置信度 0.74
• Phyre2.2: A Community Resource for Template-based Protein Structure Prediction.
  Template-based modelling, also known as homology modelling, is a powerful approach to predict the structure of a protein from its amino acid sequence. The approach requires one to identify a sequence similarity between the query sequence and that of a known st…
  europepmc 2025 置信度 0.80
• HighPlay: Cyclic Peptide Sequence Design Based on Reinforcement Learning and Protein Structure Prediction
  The structural diversity and good biocompatibility of cyclic peptides has led to their emergence as potential therapeutic agents. Traditional cyclic peptide design relies on natural template modification and combinatorial chemical library screening, but suffer…
  europepmc 2025 置信度 0.80
• Highly accurate protein structure prediction-based virtual docking pipeline accelerating the identification of anti-schistosomal compounds
  Schistosomiasis is a major neglected tropical disease that lacks an effective vaccine and faces increasing challenges from praziquantel resistance, underscoring the urgent need for novel therapeutics. Target-based drug discovery (TBDD) is a powerful strategy f…
  europepmc 2025 置信度 0.80
• Advances in Protein Structure Prediction Highlight Unexpected Commonalities Between Gram-positive and Gram-negative Conjugative T4SSs.
  Despite recent advances in our understanding of the structure and function of conjugative Type 4 Secretion Systems (T4SSs), there is still only very scarce data available for the ones from Gram-positive (G + ) bacteria. This is a problem, as conjugative T4SSs …
  europepmc 2025 置信度 0.80
• Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design
  Abstract Peptide design, with the goal of identifying peptides possessing unique biological properties, stands as a crucial challenge in peptide-based drug discovery. While traditional and computational methods have made significant strides, they often encount…
  europepmc Dong Xu 2024 置信度 0.80
• Cerebra: a computationally efficient framework for accurate protein structure prediction
  A bstract Remarkable progress has been made in the field of protein structure prediction in the past years. State-of-the-art methods like AlphaFold2 and RoseTTAFold2 achieve prediction accuracy close to experimental structural determination, but at the cost of…
  europepmc Jian Hu, Weizhe Wang, Haipeng Gong 2024 置信度 0.80
• Easy and accurate protein structure prediction using ColabFold
  Abstract Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological questions, using predicted protein structures of single monomers or full complexes, a common practice. ColabFold-AF2 is an open-source Jupyter Notebook inside Google …
  europepmc 2023 置信度 0.80
• Protein structure prediction from the complementary science perspective.
  A comparative analysis between two problems-apparently unrelated-which are solved in a period of ~400 years, viz., the accurate prediction of both the planetary orbits and the protein structures, leads to inferred conjectures that go far beyond the existence o…
  europepmc 2023 置信度 0.80
• Bilinear optimization of protein structure prediction: An exact approach via AB off-lattice model.
  Protein structure prediction (PSP) remains a central challenge in computational biology due to its inherent complexity and high dimensionality. While numerous heuristic approaches have appeared in the literature, their success varies. The AB off-lattice model,…
  europepmc 2024 置信度 0.80
• Peptide binder design with inverse folding and protein structure prediction.
  The computational design of peptide binders towards a specific protein interface can aid diagnostic and therapeutic efforts. Here, we design peptide binders by combining the known structural space searched with Foldseek, the protein design method ESM-IF1, and …
  europepmc 2023 置信度 0.80
• Starting at Go: Protein structure prediction succumbs to machine learning.
  This year's Lasker Basic Science Award recognizes the invention of AlphaFold, a revolutionary advance in the history of protein research which for the first time offers the practical ability to accurately predict the three-dimensional arrangement of amino acid…
  europepmc 2023 置信度 0.80
• Challenges in bridging the gap between protein structure prediction and functional interpretation.
  The rapid evolution of protein structure prediction tools has significantly broadened access to protein structural data. Although predicted structure models have the potential to accelerate and impact fundamental and translational research significantly, it is…
  europepmc 2025 置信度 0.80
• Progress at protein structure prediction, as seen in CASP15.
  In Dec 2020, the results of AlphaFold version 2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational method could challenge experimental accuracy for structure prediction of s…
  europepmc 2023 置信度 0.80
• Unsupervisedly Prompting AlphaFold2 for Accurate Few-Shot Protein Structure Prediction.
  Data-driven predictive methods that can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining an accurate folding landscape using coevolutiona…
  europepmc 2023 置信度 0.80
• An automated pipeline integrating AlphaFold 2 and MODELLER for protein structure prediction.
  The ability to predict a protein's three-dimensional conformation represents a crucial starting point for investigating evolutionary connections with other members of the corresponding protein family, examining interactions with other proteins, and potentially…
  europepmc 2023 置信度 0.80
• AlphaFold2 protein structure prediction: Implications for drug discovery.
  The drug discovery process involves designing compounds to selectively interact with their targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificial intellig…
  europepmc 2023 置信度 0.80
• Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XV
  Computing protein structure from amino acid sequence information has been a long-standing grand challenge. CASP (Critical Assessment of Structure Prediction) conducts community experiments aimed at advancing solutions to this and related problems. Experiments …
  europepmc 2023 置信度 0.80
• EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.
  Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing dist…
  europepmc 2024 置信度 0.80
• Recent Advances in NMR Protein Structure Prediction with ROSETTA.
  Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be …
  europepmc 2023 置信度 0.80
• A comparative study of protein structure prediction tools for challenging targets: Snake venom toxins.
  Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several protein stru…
  europepmc 2024 置信度 0.80
• Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15.
  We present the monomer and multimer structure prediction results of our methods in CASP15. We first designed an elaborate pipeline that leverages complementary sequence databases and advanced database searching algorithms to generate high-quality multiple sequ…
  europepmc 2023 置信度 0.80
• Protein structure prediction using the evolutionary algorithm USPEX.
  Protein structure prediction is one of major problems of modern biophysics: current attempts to predict the tertiary protein structure from amino acid sequence are successful mostly when the use of big data and machine learning allows one to reduce the "predic…
  europepmc 2023 置信度 0.80
• Before and after AlphaFold2: An overview of protein structure prediction.
  Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimen…
  europepmc 2023 置信度 0.80
• Protein structure prediction with energy minimization and deep learning approaches.
  In this paper we discuss the advantages and problems of two alternatives for ab initio protein structure prediction. On one hand, recent approaches based on deep learning, which have significantly improved prediction results for a wide variety of proteins, are…
  europepmc 2023 置信度 0.80
• Deep Learning for Protein Structure Prediction: Advancements in Structural Bioinformatics
  Motivation Accurate prediction of protein structures is crucial for understanding protein function, stability, and interactions, with far-reaching implications in drug discovery and protein engineering. As the fields of structural bioinformatics and artificial…
  europepmc 2023 置信度 0.80
• Challenges in Bridging the Gap Between Protein Structure Prediction and Functional Interpretation
  The rapid evolution of protein structure prediction tools has significantly broadened access to protein structural data. Although predicted structure models have the potential to accelerate and impact fundamental and translational research significantly, it is…
  europepmc 2023 置信度 0.80
• Salp-J Colony Optimization-based advanced hybrid ensemble deep predictor with LSTM for protein structure prediction.
  Protein structure prediction (PSP) is a key concern in computational biology, which is considered a challenging task that is vital to determine the structure and the protein function since each protein possesses a definite shape, whereas the protein secondary …
  europepmc 2025 置信度 0.80
• 3 = 1 + 2: how the divide conquered de novo protein structure prediction and what is next?
  Protein structure prediction has been a challenging problem for >60 years. Its progress has been considered incremental for the past 20 years until leaps were made by AlphaFold that seemed to suddenly reach experimental accuracy. We argue that this unexpected …
  europepmc 2023 置信度 0.80
• Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.
  Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack …
  europepmc 2023 置信度 0.80
• Crosslinking mass spectrometry data bolster protein structure prediction.
  europepmc 2023 置信度 0.80
• Protein structure prediction in the deep learning era.
  Significant advances have been achieved in protein structure prediction, especially with the recent development of the AlphaFold2 and the RoseTTAFold systems. This article reviews the progress in deep learning-based protein structure prediction methods in the …
  europepmc 2022 置信度 0.80
• Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Proteins?
  Understanding the conformational dynamics of proteins, such as the inward-facing (IF) and outward-facing (OF) transition observed in transporters, is vital for elucidating their functional mechanisms. Despite significant advances in protein structure predictio…
  europepmc 2023 置信度 0.80
• Protein Structure Prediction with Expectation Reflection
  Sequence covariation in multiple sequence alignments of homologous proteins has been used extensively to obtain insights into protein structure. However, global statistical inference is required in order to ascertain direct relationships between amino acid pos…
  europepmc 2022 置信度 0.80
• Protein Structure Prediction with Expectation Reflection
  Sequence covariation in multiple sequence alignments of homologous proteins has been used extensively to obtain insights into protein structure. However, global statistical inference is required in order to ascertain direct relationships between amino acid pos…
  europepmc 2022 置信度 0.80
• Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.
  Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein …
  europepmc 2023 置信度 0.80
• Evaluating the performance of protein structure prediction in detecting structural changes of pathogenic nonsynonymous single nucleotide variants
  A bstract Protein structure prediction serves as an efficient tool, saving time and circumventing the need for laborious experimental endeavors. Distinguished methodologies, including AlphaFold, RoseTTAFold, and ESMFold, have proven their precision through rig…
  europepmc 2023 置信度 0.80
• Protein structure prediction has reached the single-structure frontier.
  europepmc 2023 置信度 0.80
• LightRoseTTA: High-efficient and Accurate Protein Structure Prediction Using an Ultra-Lightweight Deep Graph Model
  Accurately predicting protein structure, from amino acid sequences to three-dimensional structures, is of great significance in biological research. To tackle this issue, a representative deep big model, RoseTTAFold, has been proposed with promising success. H…
  europepmc 2023 置信度 0.80
• Target-template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations.
  Improving protein thermostability has been a labor- and time-consuming process in industrial applications of protein engineering. Advances in computational approaches have facilitated the development of more efficient strategies to allow the prioritization of …
  europepmc 2023 置信度 0.80
• Improving protein structure prediction using templates and sequence embedding.
  Motivation Protein structure prediction has been greatly improved by deep learning, but the contribution of different information is yet to be fully understood. This article studies the impacts of two kinds of information for structure prediction: template and…
  europepmc 2023 置信度 0.80
• TemPred: A Novel Protein Template Search Engine to Improve Protein Structure Prediction.
  Among new protein structure predictors, the recently developed AlphaFold predictor relies on contact map in line with contact map potential based threading model that basically relies on fold recognition. In parallel, sequence similarity based homology model r…
  europepmc 2023 置信度 0.80
• Emerging insight of whole genome sequencing coupled with protein structure prediction into the pyrazinamide-resistance signature of Mycobacterium tuberculosis.
  Pyrazinamide (PZA) is considered to be a pivotal drug to shorten the treatment of both drug-susceptible and drug-resistant tuberculosis, but its use is challenged by the reliability of drug-susceptibility testing (DST). PZA resistance in Mycobacterium tubercul…
  europepmc 2024 置信度 0.80
• Proteomic analysis and protein structure prediction of Shigella phage Sfk20 based on a comparative study using structure prediction approaches.
  Bacteriophages are the natural predators of bacteria and are available abundantly everywhere in nature. Lytic phages can specifically infect their bacterial host (through attachment to the receptor) and use their host replication machinery to replicate rapidly…
  europepmc 2024 置信度 0.80
• Ab initio protein structure prediction: the necessary presence of external force field as it is delivered by Hsp40 chaperone.
  Background The aqueous environment directs the protein folding process towards the generation of micelle-type structures, which results in the exposure of hydrophilic residues on the surface (polarity) and the concentration of hydrophobic residues in the cente…
  europepmc 2023 置信度 0.80
• The prospects and opportunities of protein structure prediction with AI.
  europepmc 2022 置信度 0.80
• Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning.
  While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or for which few homologous sequences are known. Here we introduce AlphaLink, a modified version of the Alpha…
  europepmc 2023 置信度 0.80

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