AI 驱动科学发现 · 全部实体（第 85 / 100 页）

本页收录第 8401–8500 条，共 10000 条 · 返回 AI 驱动科学发现 首页

• Artificial Intelligence (AI) in Drug Discovery and Medicine
  Artificial intelligence (AI) is a branch of computer science that deals with the development of algorithms that seek to simulate human intelligence. The phrase “artificial intelligence” was likely coined during a conference at Dartmouth College in 1956. The ea…
  crossref Ahuja Varun 2021-01-26T11:45:21Z 置信度 0.70
• Ai-Enhanced Drug Discovery: Applying Machine Learning to Accelerate The Drug Discovery Process
  crossref Potter Kaledio, Lucas D, Ralph Shad 2024-10-23T10:31:48Z 置信度 0.70
• Strategic partnerships for AI-driven drug discovery: The role of relational dynamics
  Artificial intelligence-driven drug discovery (AIDD) companies hold significant promise for transforming pharmaceutical development, yet little is known about how they manage partnerships with established pharmaceutical firms. To address this research gap, our…
  crossref Stefan Kint, Wilfred Dolfsma, Daniela Robinson 2024-11-14T10:45:19Z 置信度 0.70
• AI drug discovery screening for COVID-19 reveals zafirlukast as a repurposing candidate.
  Aims Over the past few years, AI has been considered as potential important area for improving drug development and in the current urgent need to fight the global COVID-19 pandemic new technologies are even more in focus with the hope to speed up this process.…
  europepmc 2021 置信度 0.80
• Meta-Modeling with Drug Discovery Stack Regressor for Drug Discovery: An Explainable AI Perspective
  Introduction: Drug discovery faces persistent challenges, including the need to handle heterogeneous datasets, extended timelines, and difficulties in accurately predicting drug-target in-teractions. These issues hinder the timely development of therapeutic in…
  crossref Spoorthi J.S., Vijayalakshmi M., Sasithradevi A., Sabari Nathan 2025-10-28T09:59:21Z 置信度 0.70
• Review for "Closed-Loop Agentic AI in Drug Discovery"
  crossref 2026-07-23T16:31:07Z 置信度 0.70
• AI In Drug Discovery and Development
  Present Status ConclusionThe current research and development process includes drug identification, target verification, lead generation, lead optimization, preliminary research, and clinical study. To develop a new novel drug requires money and time both.Appr…
  crossref Hemant Vishwakarma 2024-10-01T13:20:33Z 置信度 0.70
• AI and Human Intuition in Drug Discovery
  Artificial intelligence (AI) is transforming the drug discovery process by enabling rapid analysis of complex biological data, predicting drug-target interactions, optimizing lead compounds, and accelerating candidate selection. However, human intuition, scien…
  crossref Rosa Marie 2026-06-17T06:35:27Z 置信度 0.70
• Critical assessment of AI in drug discovery
  Introduction : Artificial Intelligence (AI) has become a component of our everyday lives, with applications ranging from recommendations on what to buy to the analysis of radiology images. Many of the techniques originally developed for other fields such as la…
  crossref W. Patrick Walters, Regina Barzilay 2021-04-19T12:26:04Z 置信度 0.70
• Introduction to AI in Drug Discovery
  crossref Shaik Aminabee, Atmakuri Lakshmana Rao 2025-10-16T15:22:17Z 置信度 0.70
• CerebrumDAO Microgift CFP: AI for Drug Discovery ($5,000)
  crossref Michele Gallia 2026-01-20T19:10:26Z 置信度 0.70
• Creating an AI-first drug discovery engine
  crossref Asher Mullard 2024-09-13T09:04:52Z 置信度 0.70
• How AI is transforming drug discovery
  “We want to industrialise drug discovery,” says Imran Haque, senior vice president of AI and digital sciences at Recursion, a start-up founded in Salt Lake City, Utah, in 2013. The company’s vision is that AI will enable a much larger pipeline of drug discover…
  crossref 2024-07-03T15:43:44Z 置信度 0.70
• AI-INTEGRATED NANOMEDICINE: DISCOVERY, DESIGN, AND CLINICAL TRANSLATION
  Nanomedicine has emerged as a transformative field at the interface of materials science, biology, and digital technologies, enabling targeted delivery of small molecules, biologics, and vaccines across complex physiological barriers. Despite its promise, clin…
  crossref Dr. Rahul More 2026-03-03T09:58:09Z 置信度 0.70
• Advancing AI-driven drug discovery: an interview with Professor Alan Talevi
  Question 1: Your academic journey spans pharmacy, computational chemistry, and biopharmacy. In your recent article, you discuss the transformative role of AI in expanding confined chemical spaces. ...
  crossref Alan Talevi 2026-03-05T08:54:53Z 置信度 0.70
• Future Trends in AI-Powered Drug Discovery
  crossref Shaik Aminabee, Atmakuri Lakshmana Rao 2025-10-16T15:22:17Z 置信度 0.70
• Introduction: An Overview of AI in Oncology Drug Discovery and Development
  Artificial intelligence (AI) has been termed the machine for the fourth industrial revolution. One of the main challenges in drug discovery and development is the time and costs required to sustain the drug development pipeline. It is estimated to cost over 2.…
  crossref Kristofer Linton-Reid 2020-09-09T07:17:28Z 置信度 0.70
• AI Technologies and Tools in Drug Discovery
  crossref Shaik Aminabee, Atmakuri Lakshmana Rao 2025-10-16T15:22:17Z 置信度 0.70
• When can AI deliver the drug discovery hits?
  crossref Asher Mullard 2024-02-19T15:03:52Z 置信度 0.70
• Unleashing the power of generative AI in drug discovery
  Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug…
  crossref Amit Gangwal, Antonio Lavecchia 2024-04-23T16:19:26Z 置信度 0.70
• Case Studies of AI in Drug Discovery
  crossref Shaik Aminabee, Atmakuri Lakshmana Rao 2025-10-16T15:22:17Z 置信度 0.70
• AI in Natural Product Drug Discovery
  Revitalizing the pipeline for natural product drug discovery requires overcoming the historical challenges of structural complexity and isolation difficulty through computational innovation. Artificial Intelligence catalyzes this process by constructing and sc…
  crossref Sreedevi Kudaravalli 2025-12-27T21:01:57Z 置信度 0.70
• Physics-inspired explainable AI for mechanistic and decision support in drug discovery
  Explainable artificial intelligence (XAI) is increasingly recognized as essential for trustworthy drug discovery, yet many approaches remain post hoc and correlational. This review examines physics-inspired XAI as a framework for improving mechanistic interpre…
  crossref Antonio Lavecchia 2026-04-21T14:59:57Z 置信度 0.70
• AI for drug discovery is advancing rapidly: We need smart biology for it to fulfill its mission
  crossref Misti Ushio, Zachary Carpenter 2021-10-09T07:46:47Z 置信度 0.70
• MOLECULAR DOCKING IN DRUG DISCOVERY: THEORY, METHODS, AND AI INTEGRATION
  Molecular docking has become an indispensable technique in computer-aided drug design (CADD), offering predictive insights into how small molecules interact with biological macromolecules. By integrating computational chemistry, molecular mechanics, and thermo…
  crossref Dr. Akash Dnyaneshwar Mane, Dr. Renukacharya G. Khanapure, Dr. Laxman Appa Dhale 2026-03-03T09:58:09Z 置信度 0.70
• MOLECULAR DOCKING USING AI: FROM TRADITIONAL METHODS TO DEEP LEARNING-DRIVEN DRUG DISCOVERY
  Molecular docking has emerged as a crucial computational technique in structure-based drug design, for the prediction of protein-ligand binding interactions critical for therapeutic development. Traditional docking methods, relying on physics-based scoring fun…
  crossref Deepa N Rangadal, Rajendra R Tayade, Shubhangi P Patil 2026-03-03T09:58:09Z 置信度 0.70
• Advancing Drug Discovery through Integrative Computational Models and AI Technologies
  The intricate, costly, and time-intensive nature of traditional drug discovery processes hinders the development of novel pharmaceuticals. This research illustrates a comprehensive approach integrating advanced computational models, artificial intelligence (AI…
  crossref Stefano Piotto, Francesco Marrafino, Lucia Sessa, Eugenio Sottile 等 2024-04-12T02:35:13Z 置信度 0.70
• Integration of AI and traditional medicine in drug discovery
  AI integration in plant-based traditional medicine could be used to overcome drug discovery challenges.
  crossref Saifur R. Khan, Dana Al Rijjal, Anthony Piro, Michael B. Wheeler 2021-01-19T02:28:17Z 置信度 0.70
• Transforming drug discovery through the fusion of AI-driven analysis and protein micropatterning
  Introduction Traditional drug discovery is hampered by high costs, long timelines, and low success rates due to inefficient screening and inadequate model systems. The convergence of artificial intelligence (AI) and functional protein micropatterning offers a …
  crossref Paul Roach 2025-10-09T14:39:31Z 置信度 0.70
• Foundation models and AI agents in oncology drug discovery
  Oncology drug discovery remains limited by high attrition, slow experimental iteration and weak translation from preclinical models to clinical benefit. This review examines how AI is restructuring that process through three connected layers: biological founda…
  crossref Yashwardhan Ghanwatkar, Pankaj Rajdeo, Ram I. Mahato 2026-03-31T15:28:28Z 置信度 0.70
• 50 shades of AI in regulatory science
  crossref Weida Tong, Szczepan W. Baran 2024-06-06T16:48:16Z 置信度 0.70
• Artificial Intelligence (AI): It?s Role in Drug Discovery and Novel Drug Delivery System
  This article presents an all -encompassing overview of the pivotal role played by artificial intelligence (AI) across various stages of drug discovery and delivery, highlighting its transformative impact on the pharmaceutical industry. Emphasis is placed on th…
  crossref Chinmoyee Deori Leena Hujuri 2024-05-13T08:11:59Z 置信度 0.70
• Navigating the frontier of drug-like chemical space with cutting-edge generative AI models
  Deep generative models (GMs) have transformed the exploration of drug-like chemical space (CS) by generating novel molecules through complex, nontransparent processes, bypassing direct structural similarity. This review examines five key architectures for CS e…
  crossref Antonio Lavecchia 2024-08-03T22:41:00Z 置信度 0.70
• AI-generated in silico data in patent applications
  crossref Peter J. Finnie 2018-08-02T22:04:48Z 置信度 0.70
• AI and Machine Learning in Drug Discovery
  This chapter concerns the progress of those artificial intelligence (AI) applications and the outlook of AI in drug discovery. Drug discovery is the first and crucial step of the value chain of drug development and involves target identification, optimization,…
  crossref Harry Yang, Dai Feng, Richard Baumgartner 2022-08-12T20:20:36Z 置信度 0.70
• AI-based language models powering drug discovery and development
  The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP)…
  crossref Zhichao Liu, Ruth A. Roberts, Madhu Lal-Nag, Xi Chen 等 2021-06-30T11:26:23Z 置信度 0.70
• AI applications in drug discovery for promoting longevity
  crossref Bancha Yingngam 2026-01-31T20:05:21Z 置信度 0.70
• Data Collection and Management for AI in Drug Discovery
  crossref Shaik Aminabee, Atmakuri Lakshmana Rao 2025-10-16T15:22:17Z 置信度 0.70
• Elevating life science R&D success with AI: a framework
  crossref Ben Sidders 2024-10-22T03:38:29Z 置信度 0.70
• Can agentic AI dent Eroom's Law? Large reasoning models across the drug discovery and development pipeline.
  Productivity in pharmaceutical R&D continues to fall despite deeper biological insight and steady gains in clinical development operations - a phenomenon termed Eroom's Law. Agentic AI workflows powered by reasoning-trained large language models (LLMs), increa…
  europepmc 2026 置信度 0.80
• Author response for "Closed-Loop Agentic AI in Drug Discovery"
  crossref Pranal Chhetri 2026-07-23T16:31:07Z 置信度 0.70
• Microbiome-Aware Quantum–AI Drug Discovery for Precision Antimicrobial Design
  crossref Mark Varkevisser 2026-04-21T07:20:12Z 置信度 0.70
• Role of AI in Drug Discovery
  Background: The pharmaceutical industry is undergoing rapid digital transformation, generating vast and complex datasets that challenge traditional drug discovery workflows. Artificial intelligence (AI) has emerged as a powerful solution capable of processing …
  crossref Deependra Singh 2026-01-10T07:06:06Z 置信度 0.70
• The AI drug revolution needs a revolution
  The AI drug revolution has the potential to improve clinical successes and human health, but not if it is limited to drug and target discovery in a human-agnostic manner. Scientists should leverage AI to measure human responses to therapeutic treatments during…
  crossref Rachel DeVay Jacobson 2025-06-04T04:59:43Z 置信度 0.70
• Oncological drug discovery: AI meets structure-based computational research
  The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of…
  crossref Marina Gorostiola González, Antonius P.A. Janssen, Adriaan P. IJzerman, Laura H. Heitman 等 2022-03-14T19:25:03Z 置信度 0.70
• The unglamourous AI wins that are quietly transforming drug discovery and development.
  europepmc 2026 置信度 0.80
• Gaps between medical biology and AI drug discovery.
  Drug discovery is a complex, iterative process spanning biology, chemistry, pharmacology, and computational sciences. Artificial intelligence (AI) can accelerate this process but often misaligns with biological realities. Here, we highlight three crucial gaps …
  europepmc 2025 置信度 0.80
• Persona-driven generative AI in pharmaceuticals
  Persona-driven generative artificial intelligence (GenAI) represents a transformative approach to pharmaceutical innovation, providing structured frameworks that enhance AI-human collaboration across drug development. We examine recent applications (2023-2025)…
  crossref Yun Wan, Makoto Nakayama, Cesar Floyd Aldana, Frank Alvino 2025-11-01T15:52:16Z 置信度 0.70
• AI platform to accelerate drug discovery
  crossref Yasushi Okuno 2025-06-03T14:41:06Z 置信度 0.70
• Harnessing AI for Drug Discovery: Machine Learning Perspectives
  crossref 2025-05-27T18:08:29Z 置信度 0.70
• AI-Driven Light-Spectrum Optimization for Photonic Drug Discovery
  crossref Ndenga Lumbu Barack 2025-11-14T19:43:50Z 置信度 0.70
• AI IN DRUG DISCOVERY: SURVEY ON HOW AI CAN BE USED TO FASTEN DRUG DISCOVERY. A BIBLIOMETRIC ANALYSIS
  Background: Artificial intelligence (AI) in drug discovery is emerging as a transformative force, promising to revolutionize the development of new therapeutic agents. The increasing integration of AI techniques highlights its potential to optimize the discove…
  crossref Hira Hashmi, Dr Aboussaid Mariem, Ayesha Nazir 2025-05-29T09:05:16Z 置信度 0.70
• ARTIFICIAL INTELLIGENCE IN NATURAL PRODUCT DRUG DISCOVERY: ADVANCEMENTS, CHALLENGES, AND FUTURE PERSPECTIVES
  Natural products (NPs) have been crucial to pharmaceutical discovery for millennia, offering structurally varied and bioactive compounds that meet intricate medical requirements. Recent advancements in Artificial Intelligence (AI) have revolutionized NP resear…
  crossref Dr. Dattatraya Bhange 2026-03-03T09:58:09Z 置信度 0.70
• Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods
  The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (…
  crossref Chinmayee Choudhury, N. Arul Murugan, U. Deva Priyakumar 2022-03-14T17:19:54Z 置信度 0.70
• AI-Assisted Drug Discovery from Phytoconstituents: Predicting Drug–Target Interactions
  Artificial intelligence (AI) has emerged as a powerful computational tool to support early-stage drug discovery by facilitating the analysis and prioritization of bioactive compounds. In the context of plant-derived phytoconstituents, AI-based methods offer si…
  crossref Muskan Tomar 2026-02-18T23:09:43Z 置信度 0.70
• Introduction of AI in drug design
  crossref Shuheng Huang, Tianmiao Ou 2026-05-08T09:24:19Z 置信度 0.70
• AI and Computational Chemistry in Phytochemical Drug Discovery
  The landscape of modern drug discovery is undergoing a paradigm shift, with computational tools at the forefront of transforming the way new therapeutics are identified, developed, and optimized. Traditional methods of drug discovery, often reliant on serendip…
  crossref P. Selvakumar, T.C. Manjunath, R. Mythili 2025-11-20T19:07:36Z 置信度 0.70
• The Evolving Role of the Qualified Person in an AI-Driven Pharma Compliance Landscape
  crossref Pete Gough 2026-03-28T00:44:58Z 置信度 0.70
• Author Correction: The AI drug revolution needs a revolution
  In the sentence beginning ‘As mentioned above, the probability that a program will fail in the clinic is high...’ in this article, the text ‘which makes it is difficult to hold companies accountable when their programs don’t maintain rigorous efficacy standard…
  crossref Rachel DeVay Jacobson 2025-06-28T08:53:34Z 置信度 0.70
• Promises of text processing: natural language processing meets AI
  crossref Jeffrey T Chang, Russ B Altman 2002-10-08T11:21:12Z 置信度 0.70
• Chapter 12. AI <i>via</i> Matched Molecular Pair Analysis
  Matched molecular pair analysis (MMPA) is a well accepted, transparent SAR analysis method that also enables the generation of new molecules to address a biological or physicochemical goal. This makes it an ideal component of a compound discovery artificial in…
  crossref Ed Griffen, Alexander Dossetter, Andrew G. Leach 2020-11-04T11:40:45Z 置信度 0.70
• Generative AI in drug discovery and development: the next revolution of drug discovery and development would be directed by generative AI
  Dear Editor, Previously, we published a correspondence article in International Journal of Surgery about next-generation drug discovery and development using ChatGPT or Large Language Model (LLM)1. The article was very timely. However, we found that generative…
  crossref Chiranjib Chakraborty, Manojit Bhattacharya, Soumen Pal, Md. Aminul Islam 2024-10-01T11:01:48Z 置信度 0.70
• AI and drug discovery : Search for new psychoactive candidates from cannabis sativa
  crossref Ramesh Jagannathan 2020-03-31T19:24:13Z 置信度 0.70
• SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability
  Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI’s rapid adoption, its integration into scientifi…
  crossref 2024-08-23T11:43:28Z 置信度 0.70
• AI-driven Drug Discovery in Bioinformatics: Accelerating Pharmaceutical Research
  AI-driven Drug Discovery in Bioinformatics: AcceleratingPharmaceutical Research
  crossref Hassan Ali 2023-08-08T05:01:03Z 置信度 0.70
• Integrating Generative AI with High-Throughput Screening for Accelerated Drug Discovery
  crossref Itunuoluwa Adegbola 2025-07-01T13:32:37Z 置信度 0.70
• AI-Optimized Photon-Assisted Molecular Docking for Rapid Drug Discovery
  crossref Ndenga Lumbu Barack 2025-10-23T15:23:02Z 置信度 0.70
• Toward the Clinic: Understanding Patient Perspectives on AI and Data-Sharing for AI-Driven Oncology Drug Development
  The increasing application of AI-led systems for oncology drug development and patient care holds the potential to usher pronounced impacts for patients’ well-being. Beyond technical innovations and infrastructural adjustments, research suggests that realizing…
  crossref Roberta Dousa 2020-09-09T07:17:28Z 置信度 0.70
• AI-driven drug discovery: A boon against COVID-19?
  The COVID-19 is an issue of international concern and threat to public health and there is an urgent need of drug/vaccine design. There is no vaccine or specific drug yet made as of July 23, 2020, for the coronavirus disease (COVID-19). Thus, the patients curr…
  crossref Aman Chandra Kaushik, Utkarsh Raj 2020-08-03T04:58:06Z 置信度 0.70
• Graph Neural Networks in Modern AI-Aided Drug Discovery
  Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on molecular graphs, GNNs offer an intuitive and expressive framework for learning t…
  crossref 2025-09-17T10:00:35Z 置信度 0.70
• An introduction to GitHub and its significance for AI-driven drug discovery
  Introduction GitHub has become essential to AI-driven drug discovery by facilitating code sharing, collaboration, and reproducible workflows. As more tools for screening, modeling, and data-driven decision-making are hosted on GitHub, researchers need clear, r…
  crossref Abdallah Abou Hajal, Lana Bustanji, Richard A. Bryce, Mohammad A. Ghattas 2026-03-05T15:24:07Z 置信度 0.70
• Democratising real-world drug discovery through agentic AI
  Agentic systems that are based on large language models (LLMs) have emerged as promising tools in the chemistry domain over the past few years. Early examples included work on CoScientist, Chemcrow, and LLM-RDF, which showcased the potential of agentic systems…
  crossref Jiazhen He, Helen Lai, Lakshidaa Saigiridharan, Gian Marco Ghiandoni 等 2026-01-16T07:44:55Z 置信度 0.70
• Targeting EZH2 in Cancer: AI-Driven Pipeline for Drug Discovery and Optimization
  Traditional drug discovery is time-intensive and costly, often spanning over a decade and incurring billions in expenses. This study introduces a novel machine learning pipeline tailored to predict and optimize inhibitors for Enhancer of Zeste Homolog 2 (EZH2)…
  crossref April Surac 2025-01-07T11:45:43Z 置信度 0.70
• Learning from the successes and failures of early artificial intelligence (AI) adoption for drug discovery in Big BioPharma
  Introduction AI has tremendous potential to reduce time and costs taken to discover and develop new medical entities. As technology evolves, it is essential to learn from successes and failures to realign expectations for scientists, stakeholders and investors…
  crossref Martin Braddock, Krzysztof Jeziorski 2026-01-30T13:03:56Z 置信度 0.70
• AI-native drug design: A five-year agenda for mechanistic, regulatable, and globally equitable innovation
  crossref M. Vijayasimha 2026-02-02T20:10:19Z 置信度 0.70
• MARINE ALKALOIDS IN DRUG DISCOVERY AND ARTIFICIAL INTELLIGENCE
  Marine alkaloids are an enriching source of bioactive compounds, getting attention for their isolation and identification towards novel drug analogs. However, their low supply, lengthy and costly traditional methods of identification, isolation, and optimizati…
  crossref Sakshi Agale, Madhura Khose, Sakshi Kumbhar, Shivani Shinde 等 2026-03-03T09:58:09Z 置信度 0.70
• Understanding nuclear receptors using computational methods
  Nuclear receptors (NRs) are important targets for therapeutic drugs. NRs regulate transcriptional activities through binding to ligands and interacting with several regulating proteins. Computational methods can provide insights into essential ligand-receptor …
  crossref Ni Ai, Matthew D. Krasowski, William J. Welsh, Sean Ekins 2009-03-12T04:23:52Z 置信度 0.70
• Key Drivers of AI Drug Discovery
  crossref Kevin Davies 2024-12-10T11:23:03Z 置信度 0.70
• Revolutionizing High Content Analysis: Advancing Drug Discovery by combining complex in vitro models with AI-Powered 3D/4D image analysis
  The FDA's approval of organoids in preclinical research, under the FDA Modernization Act 2.0, has catalyzed a major shift toward complex in vitro models (CIVM) for drug testing and disease research. The implementation of more biologically relevant phenotypic m…
  crossref Delisa Garcia, Martin Engel 2025-02-06T14:46:29Z 置信度 0.70
• Daily briefing: New AI drug-discovery engine is 'on the scale of an AlphaFold4'.
  europepmc 2026 置信度 0.80
• AI Drug Discovery: Expanding the Horizons of Infectious Disease Therapeutics.
  Drug discovery and development for infectious diseases has transformed from phenotypic screening to rational design, and now embracing artificial intelligence (AI) to accelerate and optimize therapeutic development. We describe our use of AI to analyze vast an…
  europepmc 2025 置信度 0.80
• Toward Unified AI Drug Discovery with Multimodal Knowledge.
  Background: In real-world drug discovery, human experts typically grasp molecular knowledge of drugs and proteins from multimodal sources including molecular structures, structured knowledge from knowledge bases, and unstructured knowledge from biomedical lite…
  europepmc 2024 置信度 0.80
• Upswing in AI drug-discovery deals.
  europepmc 2023 置信度 0.80
• AI drug discovery booms in China.
  europepmc 2021 置信度 0.80
• Shake-up in AI drug discovery.
  europepmc 2019 置信度 0.80
• Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges.
  Medicinal plants represent a vast and evolutionarily refined reservoir of structurally diverse bioactive compounds that have historically contributed to major therapeutic breakthroughs. However, despite their pharmacological richness, systematic translation of…
  europepmc 2026 置信度 0.80
• AI-driven computational drug design: tools, workflow and challenges.
  Drug discovery is a time-consuming and resource-intensive process with a development period of more than ten years and a clinical attrition rate of more than 90%. Despite its contributions to rational drug design, computer-aided drug design has been constraine…
  europepmc 2026 置信度 0.80
• Can Artificial intelligence meaningfully shorten drug discovery timelines? Current evidence, roadblocks and future directions.
  Introduction Artificial intelligence (AI) is increasingly proposed as a means of shortening drug-discovery timelines, although its practical impact varies across the discovery and development process. A critical review is needed to distinguish the stages in wh…
  europepmc 2026 置信度 0.80
• Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.
  The dual activating potential of peroxisome proliferator-activated receptors (PPAR) α and γ offers a promising way to address metabolic issues, neuroinflammation, and protein balance in the development of Alzheimer's disease (AD). The current study intends to …
  europepmc 2026 置信度 0.80
• Artificial Intelligence in Selected Domains of Drug Discovery: A Critical Narrative Review.
  Introduction Traditional drug discovery is historically characterized by high attrition rates, escalating financial costs, and decades-long development timelines. As global health challenges-particularly antimicrobial resistance and complex malignancies-intens…
  europepmc 2026 置信度 0.80
• The use of artificial intelligence (AI) in neuropsychiatric drug discovery: current challenges and future directions.
  Artificial intelligence (AI) has emerged as a powerful tool for solving real world problems across a wide range of industries and is increasingly being utilised by pharmaceutical companies to discover novel drug targets, biomarkers, and new drugs. Several AI-d…
  europepmc 2026 置信度 0.80
• Emerging Trends in Artificial Intelligence-Driven Drug Discovery: Balancing Chemical Design and Pharmacological Properties for Cancer Therapy.
  Traditional cancer drug discovery encounters challenges, including lengthy synthesis durations, high costs, and a 90% failure rate in clinical trials, primarily due to inadequate chemical design and drug properties. Artificial intelligence (AI) provides powerf…
  europepmc 2026 置信度 0.80
• AI agents in drug discovery: A review of evolution, applications, and future directions.
  Artificial intelligence (AI) agents represent a paradigm shift in pharmaceutical research, moving the field from narrow drug-protein affinity modeling toward systems-biology-level evaluation in which autonomous, multi-domain agents combine pattern recognition …
  europepmc 2026 置信度 0.80
• Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.
  Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads…
  europepmc 2026 置信度 0.80
• Artificial Intelligence in Pharmaceutical Care: &lt;i&gt;Transforming Drug Discovery, Development, and Patient-care Delivery&lt;/i&gt;.
  The integration of artificial intelligence (AI) into pharmaceutical care represents a paradigm shift in healthcare delivery, offering unprecedented opportunities to revolutionize medication management, accelerate drug development, and enhance patient outcomes.…
  europepmc 2026 置信度 0.80
• Convergence of cryo-electron microscopy and artificial intelligence in integrative structural biology: A critical review of advances, synergies, and implications for molecular biophysics and drug discovery.
  Structural biology has entered a period of rapid methodological change defined by the convergence of cryo-electron microscopy (cryo-EM) and artificial intelligence (AI)-driven structure prediction. Prior reviews have generally addressed cryo-EM instrumentation…
  europepmc 2026 置信度 0.80
• Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations.
  Computer-aided drug discovery (CADD) has become an integral component of modern drug development, supporting hit identification, lead optimisation, and candidate refinement across both academia and industry. Over 4 decades of methodological progress have contr…
  europepmc 2026 置信度 0.80
• From Virtual Molecules to Clinical Trials: How AI Is Reshaping Preclinical Drug Discovery.
  Unlabelled Pharmaceutical development is typically a long and arduous process, but advances in artificial intelligence may help streamline each stage of this pipeline. In this News and Perspectives article, JMIR Correspondent Benedette Cuffari reports on recen…
  europepmc 2026 置信度 0.80
• Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.
  Artificial intelligence (AI) enhances the precision, personalization, and efficiency of cancer treatment through deep learning and machine learning techniques. This review comprehensively examines the evolution of AI and its expanding applications in cancer ra…
  europepmc 2026 置信度 0.80
• TPPS4 inhibits PEDV by stabilizing viral RNA G-quadruplex and promoting ER stress: a transfer-learning-driven discovery.
  Porcine epidemic diarrhea virus (PEDV) is a devastating enteric pathogen that causes substantial economic losses in the global swine industry. While PEDV inhibitors offer a promising alternative to compensate for vaccine evasion caused by viral mutations, thei…
  europepmc 2026 置信度 0.80