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• M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery
  We introduce M$^2$Hub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especially biomolecules for drug discovery. However, the development of machine learning…
  arxiv Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li 等 2023-06-14T23:06:36Z 置信度 0.78 cond-mat.mtrl-scics.LG
• Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest
  This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and composites with large com…
  arxiv Baldur Steingrimsson, Xuesong Fan, Anand Kulkarni, Michael C. Gao 等 2020-12-05T19:32:39Z 置信度 0.78 cond-mat.mtrl-scics.LG
• Correction to: Machine Learning for Materials Discovery
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-11-01T10:06:48Z 置信度 0.70
• Machine Learning in the Discovery of New Materials
  crossref Ruichen Tian 2022-06-07T04:49:49Z 置信度 0.70
• Towards Machine Learning Assisted Discovery of Organic Topological Materials
  This perspective article describes our vision and proposal for the design and implementation of organic topological materials by using machine learning approach. We propose integrating advanced machine learning approaches with the state-of-the-art electronic s…
  crossref Muhammad Usman 2025-03-05T00:38:46Z 置信度 0.70
• Review for "The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?"
  crossref 2025-12-05T21:08:51Z 置信度 0.70
• Decision letter for "The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?"
  crossref 2025-12-05T21:08:51Z 置信度 0.70
• Machine learning guided discovery of Non-Linear Optical materials
  Nonlinear optical(NLO) materials are crucial in achieving desired frequencies in solid-state lasers. So far, new NLO materials have been discovered using high-throughput calculations or chemical intuition. This study demonstrates the effectiveness of utiliz- i…
  crossref Sownyak Mondal, Raheel Hammad 2024-08-12T08:34:05Z 置信度 0.70
• Discovery of 2D Materials with Machine Learning
  Discovery of two-dimensional (2D) materials has gained significant attention in recent years due to their unique physical and chemical properties. In this chapter, machine learning techniques were explored to accelerate the discovery process of 2D materials. M…
  crossref Md Mahmudul Hasan, Rabbi Sikder, Bharat K. Jasthi, Etienne Z. Gnimpieba 等 2023-12-04T12:44:20Z 置信度 0.70
• Advancing magnetic material discovery through machine learning: Unveiling new manganese-based materials
  Magnetic materials are used in a variety of applications, such as electric generators, speakers, hard drives, MRI machines, etc. Discovery of new magnetic materials with desirable properties is essential for advancement in these applications. In this research …
  crossref Yogesh Khatri, Arti Kashyap 2023-12-01T14:17:30Z 置信度 0.70
• Machine Learning-Driven Materials Discovery: Unlocking Next-Generation Functional Materials – a Minireview
  crossref Dilshod Nematov, Mirabbos Hojamberdiev 2025-04-16T18:39:36Z 置信度 0.70
• A Machine Learning aided hierarchical screening strategy for materials discovery [Slides]
  Machine learning can be used to screen millions of compounds and unearth relationships between electronic structure, chemistry, thermodynamic stability, formability and band gap. We know how to calculate thermodynamic stability and calculate approximate band g…
  crossref Anjana Talapatra 2021-08-06T02:52:45Z 置信度 0.70
• Material Discovery
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Machine-Learning-Assisted Materials Discovery from Electronic Band Structure
  Traditional methods of materials discovery, often relying on intuition and trial-and-error experimentation, are time-consuming and limited in their ability to explore the vast design space effectively. The emergence of machine learning (ML) as a powerful tool …
  crossref 2024-11-03T14:00:17Z 置信度 0.70
• Interpretable Machine Learning
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Introduction to Machine Learning
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials
  We\ndiscover many new crystalline solid materials with fast single\ncrystal Li ion conductivity at room temperature, discovered through\ndensity functional theory simulations guided by machine learning-based\nmethods. The discovery of new solid Li superionic c…
  crossref 2020-04-10T08:08:14Z 置信度 0.70
• Combined evolutionary optimization and machine learning for efficient discovery of polymer materials
  Macromolecular Chemistry: The Second Century [Author Benefits](https://scimeetings.acs.org/?utm_source=pubs_content_marketing&utm_medium=website&utm_campaign=0320_MCF_NPI_Launch_Spring_Homepage&ref=pubs_content_marketing) [How to use SciMeetings](https://stora…
  crossref Geoffrey Hutchison, Danielle Hiener 2020-04-05T20:57:12Z 置信度 0.70
• Author response for "The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?"
  crossref Matthew Walker, Keith Butler 2025-12-05T21:08:51Z 置信度 0.70
• Deep Learning
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Discovery of Novel Photocatalysts Using Machine Learning Approach
  Machine learning (ML) in photocatalysis research is still at its very beginning. Given recent developments, it is clear that computational screening techniques are a powerful and efficient means for achieving accelerated discovery of new materials. This is tru…
  crossref G. Sudha Priyanga, Gaurav Pransu, Harshita Krishna, Tiju Thomas 2023-05-22T20:35:09Z 置信度 0.70
• Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning
  Searching\nfor novel, high-performance, two-dimensional photovoltaic\n(2DPV) materials is an important pursuit for solar cell applications.\nIn this work, an efficient method based on the machine learning algorithm\ncombined with high-throughput screening is d…
  crossref 2020-04-07T19:39:00Z 置信度 0.70
• A Machine-Learning-Assisted Crystalline Structure Prediction Framework To Accelerate Materials Discovery
  Modern crystal structure prediction methods based on structure generation algorithms and first-principles calculations play important roles in the design of new materials. However, the cost of these methods is very expensive because their success mostly relies…
  crossref 2024-07-08T14:10:13Z 置信度 0.70
• Decision letter for "Machine learning based feature engineering for thermoelectric materials by design"
  crossref 2024-09-27T00:05:47Z 置信度 0.70
• A Machine Learning Platform for the Discovery of Materials
  Abstract For photovoltaic materials, properties such as band gap E g are critical indicators of the material's suitability to perform a desired function. Calculating E g is often performed using Density Function Theory ( DFT ) methods, although more accurate c…
  crossref Carl Belle, Vural Aksakalli, Salvy Russo 2020-12-05T00:29:29Z 置信度 0.70
• Machine Learning-Assisted Discovery of High-Voltage Organic Materials for Rechargeable Batteries
  Organic\nredox compounds are rich in elements and structural diversity,\nwhich are an ideal choice for lithium-ion batteries. However, most\norganic cathode materials show a trade-off between specific capacity\nand voltage, limiting energy density. By increasi…
  crossref 2021-09-28T18:31:09Z 置信度 0.70
• Multimodal Machine Learning for Materials Science: Discovery of Novel Li-Ion Solid Electrolytes
  The widespread adoption of multimodal machine learning (ML) models such as GPT-4 and Gemini has revolutionized various research domains, including computer vision and natural language processing. However, their implementation in materials informatics remains u…
  crossref 2024-11-29T12:30:16Z 置信度 0.70
• Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
  Accelerated discovery with machine\nlearning (ML) has begun to provide\nthe advances in efficiency needed to overcome the combinatorial challenge\nof computational materials design. Nevertheless, ML-accelerated discovery\nboth inherits the biases of training d…
  crossref 2021-05-11T14:33:17Z 置信度 0.70
• Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods
  Solar\nenergy plays an important role in solving serious environmental\nproblems and meeting the high energy demand. However, the lack of\nsuitable materials hinders further progress of this technology. Here,\nwe present the largest inorganic solar cell materi…
  crossref 2020-04-08T14:19:49Z 置信度 0.70
• Machine Learning-Aided Screening and Design Rule Discovery for LWIR-Transparent Optical Materials
  The development of low-cost, high-performance materials with enhanced transparency in the long-wavelength infrared (LWIR) region (800–1250 cm–1/8–12.5 μm) is essential for advancing thermal imaging and sensing technologies. Traditional LWIR optics rely on cost…
  crossref 2025-09-10T02:30:14Z 置信度 0.70
• Machine Learning for Materials Discovery
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning
  Organic electrode materials (OEMs), composed of abundant elements such as carbon, nitrogen, and oxygen, offer sustainable alternatives to conventional electrode materials that depend on finite metal resources. The vast structural diversity of organic compounds…
  crossref 2024-11-01T07:10:23Z 置信度 0.70
• 489Automated materials characterization using machine learning for screening biocompatible materials
  Currently, biomedical engineering biomedical engineering has received a boost, largely due to the rate at which new biocompatible materials have been developed, offering innovative solutions to many biomedical application areas. This chapter focuses on the con…
  crossref V. Vaishnavi, G. Meena, Santhanalakshmi, R. Anitha 等 2025-09-30T10:06:27Z 置信度 0.70
• Decision letter for "Machine-learning-assisted discovery of lattice dynamics signatures of sodium superionic conductors"
  crossref 2025-09-18T21:10:37Z 置信度 0.70
• Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation
  Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are in…
  crossref 2024-12-16T11:14:41Z 置信度 0.70
• Machine Learning Accelerates the Discovery of Light-Absorbing Materials for Double Perovskite Solar Cells
  Double\nperovskite materials have excellent electronic and optical\nproperties, which are the star material in the photovoltaic field.\nHowever, the large number of family members has brought difficulties\nto traditional material screening methods. The emergen…
  crossref 2021-10-13T20:05:54Z 置信度 0.70
• Machine Learned Material Simulation
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Machine Learning in X-ray Scattering for Materials Discovery and Characterization
  X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structur…
  crossref Connor Davel, Nazanin Bassiri-Gharb, Juan-Pablo Correa-Baena 2024-10-21T04:22:42Z 置信度 0.70
• Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods
  Machine\nlearning (ML) is increasingly becoming a helpful tool in\nthe search for novel functional compounds. Here we use classification\nvia random forests to predict the stability of half-Heusler (HH) compounds,\nusing only experimentally reported compounds …
  crossref 2020-04-06T14:40:00Z 置信度 0.70
• Interpretable ML for Materials
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Machine Learning Framework for Polymer Discovery
  crossref Cheng Yan, Guoqiang Li 2021-11-12T17:54:06Z 置信度 0.70
• Discovery Precision: An effective metric for evaluating performance of machine learning model for explorative materials discovery
  crossref Zhengheng Lian, Yingying Ma, Minjie Li, Wencong Lu 等 2023-12-21T05:31:31Z 置信度 0.70
• Decision letter for "Reproducibility in materials informatics: lessons from 'A general-purpose machine learning framework for predicting properties of inorganic materials'"
  crossref 2024-09-27T00:03:56Z 置信度 0.70
• Applications of Machine Learning for Representing Interatomic Interactions
  Machine learning focuses on prediction, based on known properties learned from training data. In computational materials science, this powerful technique is often used for constructing new interatomic potentials. These approaches are illustrated in this chapte…
  crossref Alexander V. Shapeev 2018-10-30T17:00:05Z 置信度 0.70
• Introduction
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• A materials map integrating experimental and computational data via graph-based machine learning for enhanced materials discovery
  Materials informatics (MI), emerging from the fusion of materials science and data science, has the potential to greatly accelerate material development and discovery. Although MI relies on data from both computational and experimental studies, their integrati…
  crossref Y. Hashimoto, X. Jia, H. Li, T. Tomai 2025-07-28T16:18:22Z 置信度 0.70
• Machine Learning and First-Principles Discovery of Ternary Superhard Materials
  Superhard compounds defined as those with a Vickers hardness H V > 40 GPa have a wide range of industrial and extreme-environment applications. However, unlike other mechanical properties such as bulk and shear moduli, the hardness of a material is less well-d…
  crossref Wei-Chih Chen, Da Yan, Cheng-Chien Chen 2022-06-14T13:38:11Z 置信度 0.70
• Figure 2: Machine learning-driven biomarker discovery.
  crossref 2026-02-03T08:34:00Z 置信度 0.70
• Analysis and evaluation of machine learning applications in materials design and discovery
  crossref Mahsa Golmohammadi, Masoud Aryanpour 2023-01-25T17:59:54Z 置信度 0.70
• Explainable Machine Learning and Visual Knowledge Discovery
  crossref Boris Kovalerchuk 2023-08-17T08:02:17Z 置信度 0.70
• Accelerating Materials Discovery of Novel Europium(II)-Activated Phosphors through Machine Learning Classification of Europium Valences
  An approach is presented to accelerate the discovery of host compounds for novel Eu2+-activated phosphor materials by integrating systematic data collection, machine learning, and experimental validation. A data set of Eu2+- and Eu3+-activated phosphors has be…
  crossref 2024-11-25T12:40:11Z 置信度 0.70
• (Invited) Machine Learning in Materials Discovery
  Machine learning (ML) is rapidly emerging as an important tool for materials discovery. In this talk, we will address key considerations involved in applying ML to materials design problems, including: using sequential learning for inverse design of materials …
  crossref Bryce Meredig 2020-02-27T02:07:06Z 置信度 0.70
• Decision letter for "Machine learning accelerated discovery of multimetallic catalyst for ethylene glycol diamination"
  crossref 2026-07-31T21:04:11Z 置信度 0.70
• Model Refinement
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Property Prediction
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• 395Machine learning innovations in biomedical materials from drug discovery to personalized medicine
  Machine learning (ML) machine learning has also introduced new paradigms to biomedical materials science for new material discovery, material optimization, and customized treatment. Pertinent to this, this chapter presents a comprehensive review of the progres…
  crossref P. Nagarajan, D. David Neels Ponkumar 2025-09-30T10:06:27Z 置信度 0.70
• Image-Based Predictions
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Natural Language Processing
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Quantum machine learning in drug discovery
  Τα φάρμακα μέσω της αλληλεπίδρασης τους κυρίως με συγκεκριμένες πρωτεΐνες-στόχους, ρυθμίζουν βιοχημικά μονοπάτια και φυσιολογικές λειτουργίες με στόχο την πρόληψη και τη θεραπεία των διαφόρων ασθενειών. Το αυξανόμενο κόστος και οι μακροχρόνιοι περίοδοι ανάπτυξ…
  crossref Μαρία Αβραμούλη 2025-02-11T09:26:12Z 置信度 0.70
• Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning
  In this study, a framework for predicting the gas-sensitive properties of gas-sensitive materials by combining machine learning and density functional theory (DFT) has been proposed. The framework rapidly predicts the gas response of materials by establishing …
  crossref Shasha Gao, Yongchao Cheng, Lu Chen, Sheng Huang 2024-06-24T19:02:18Z 置信度 0.70
• Material discovery and modeling acceleration via machine learning
  This paper delves into the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in materials science, spotlighting their capability to expedite the discovery and development of newer, more efficient, and stronger compounds. It undersco…
  crossref Carmine Zuccarini, Karthikeyan Ramachandran, Doni Daniel Jayaseelan 2024-09-04T17:46:26Z 置信度 0.70
• Machine learning in drug discovery: A practical introduction
  crossref 2021-03-24T11:54:15Z 置信度 0.70
• Parametric Methods for Regression
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Editorial: Machine Learning and Discovery
  crossref Pat Langley, Ryszard S. Michalski 2003-04-04T16:57:10Z 置信度 0.70
• Data Visualization and Preprocessing
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Dimensionality Reduction and Clustering
  crossref N. M. Anoop Krishnan, Hariprasad Kodamana, Ravinder Bhattoo 2024-05-06T21:01:45Z 置信度 0.70
• Machine Discovery of Protein Motifs
  crossref Darrell Conklin 2003-04-04T16:55:36Z 置信度 0.70
• Optimizing the Electrocatalytic Discovery with Machine Learning as a Novel Paradigm
  crossref Afshan Hassan Wani, Ajit Sharma 2024-09-13T08:02:12Z 置信度 0.70
• Decision letter for "Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials from physical properties"
  crossref 2024-09-27T00:04:09Z 置信度 0.70
• Machine Learning in Porous Materials: SVM-Based Characterization and CGAN-Driven Materials Discovery and Design
  This chapter begins with an introduction, citing the relevant literature to explain the background, practice, and future challenges of applying machine learning to the characterization, materials discovery, and design of porous materials. It is followed by two…
  crossref Shinichi Ookawara, Tomoki Yasuda, Yosuke Matsuda, Shiro Yoshikawa 等 2022-06-14T13:38:11Z 置信度 0.70
• Machine Learning in Drug Discovery and Development
  crossref 2022-06-28T08:40:09Z 置信度 0.70
• Machine Learning Applications in Drug Discovery and Development
  Machine learning has emerged as a powerful tool in the field of drug discovery and development, revolutionizing the way pharmaceutical research is conducted. This abstract provides a concise overview of the key applications and impacts of machine learning in t…
  crossref Sushma D 2024-01-24T04:25:32Z 置信度 0.70
• Evolution of MG AZ31 twin activation with strain: A machine learning study
  crossref Andrew D. Orme, David T. Fullwood, Michael P. Miles, Christophe Giraud-Carrier 2018-09-28T14:27:21Z 置信度 0.70
• Crystal Symmetry and Machine Learning for Systematic Materials Discovery
  Discovering new crystalline materials lies at the frontier of modern materials science, driving innovation in energy storage, catalysis, semiconductors, and beyond. The vastness of the chemical and structural space poses a profound challenge: the number of pos…
  crossref Abhijith S. Parackal 2026-02-13T22:01:55Z 置信度 0.70
• Accelerated Discovery of Thermoelectric Materials Using Machine Learning
  crossref Rinkle Juneja, Abhishek K. Singh 2021-03-26T09:02:46Z 置信度 0.70
• WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
  While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework,…
  arxiv Yunchao Liu, Ha Dong, Xin Wang, Rocco Moretti 等 2024-11-14T21:49:41Z 置信度 0.78 cs.LGcs.AIq-bio.BM
• Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey
  The integration of Artificial Intelligence (AI) into the field of drug discovery has been a growing area of interdisciplinary scientific research. However, conventional AI models are heavily limited in handling complex biomedical structures (such as 2D or 3D p…
  arxiv Zhiqiang Zhong, Anastasia Barkova, Davide Mottin 2023-02-16T12:38:01Z 置信度 0.78 cs.LG
• DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery
  Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-eff…
  arxiv Tianyu Liu, Sihan Jiang, Fan Zhang, Kunyang Sun 等 2026-02-11T19:16:33Z 置信度 0.78 cs.LGcs.AIcs.SEq-bio.BM
• Energy-based Generative Models for Target-specific Drug Discovery
  Drug targets are the main focus of drug discovery due to their key role in disease pathogenesis. Computational approaches are widely applied to drug development because of the increasing availability of biological molecular datasets. Popular generative approac…
  arxiv Junde Li, Collin Beaudoin, Swaroop Ghosh 2022-12-05T16:41:36Z 置信度 0.78 cs.LG
• Causal inference in drug discovery and development
  To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision making in drug disc…
  arxiv Tom Michoel, Jitao David Zhang 2022-09-29T09:54:18Z 置信度 0.78 q-bio.QMcs.LGstat.AP
• LIDDIA: Language-based Intelligent Drug Discovery Agent
  Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space of potential therapies. Recent advances in artificial intelligence for chemistry have sought to expedite individ…
  arxiv Reza Averly, Frazier N. Baker, Ian A. Watson, Xia Ning 2025-02-19T18:56:12Z 置信度 0.78 cs.CL
• Data-centric challenges with the application and adoption of artificial intelligence for drug discovery
  Introduction: Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting the impact and scope of AI models. Areas covered: In this…
  arxiv Ghita Ghislat, Saiveth Hernandez-Hernandez, Chayanit Piyawajanusorn, Pedro J. Ballester 2024-07-06T18:37:33Z 置信度 0.78 q-bio.OT
• MARS: A neurosymbolic approach for interpretable drug discovery
  Background: Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared to neural approaches, NeSy methods often possess enhanced interpretability, which is particularly promising for …
  arxiv Lauren Nicole DeLong, Yojana Gadiya, Paola Galdi, Jacques D. Fleuriot 等 2024-10-02T14:14:17Z 置信度 0.78 cs.AIcs.LGcs.LO
• Drug Discovery SMILES-to-Pharmacokinetics Diffusion Models with Deep Molecular Understanding
  Artificial intelligence (AI) is increasingly used in every stage of drug development. One challenge facing drug discovery AI is that drug pharmacokinetic (PK) datasets are often collected independently from each other, often with limited overlap, creating data…
  arxiv Bing Hu, Anita Layton, Helen Chen 2024-08-14T16:01:02Z 置信度 0.78 q-bio.QMcs.AIcs.LG
• Accelerating drug discovery with Artificial: a whole-lab orchestration and scheduling system for self-driving labs
  Self-driving labs are transforming drug discovery by enabling automated, AI-guided experimentation, but they face challenges in orchestrating complex workflows, integrating diverse instruments and AI models, and managing data efficiently. Artificial addresses …
  arxiv Yao Fehlis, Paul Mandel, Charles Crain, Betty Liu 等 2025-04-01T17:22:50Z 置信度 0.78 cs.SEcs.AI
• A Survey of Graph Neural Networks for Drug Discovery: Recent Developments and Challenges
  Graph Neural Networks (GNNs) have gained traction in the complex domain of drug discovery because of their ability to process graph-structured data such as drug molecule models. This approach has resulted in a myriad of methods and models in published literatu…
  arxiv Katherine Berry, Liang Cheng 2025-09-09T16:09:00Z 置信度 0.78 cs.LG
• A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective
  Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) hav…
  arxiv Stephen Bonner, Ian P Barrett, Cheng Ye, Rowan Swiers 等 2021-02-19T17:49:38Z 置信度 0.78 cs.AI
• Innovative in silico approaches to address avian flu using grid technology
  The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is rela…
  arxiv V. Vincent Breton, A. L. Da Costa, P. De Vlieger, L. Maigne 等 2008-12-23T07:06:36Z 置信度 0.78 q-bio.PE
• Drug Discovery with Dynamic Goal-aware Fragments
  Fragment-based drug discovery is an effective strategy for discovering drug candidates in the vast chemical space, and has been widely employed in molecular generative models. However, many existing fragment extraction methods in such models do not take the ta…
  arxiv Seul Lee, Seanie Lee, Kenji Kawaguchi, Sung Ju Hwang 2023-10-02T01:30:42Z 置信度 0.78 cs.LG
• A Spoken Drug Prescription Dataset in French for Spoken Language Understanding
  Spoken medical dialogue systems are increasingly attracting interest to enhance access to healthcare services and improve quality and traceability of patient care. In this paper, we focus on medical drug prescriptions acquired on smartphones through spoken dia…
  arxiv Ali Can Kocabiyikoglu, François Portet, Prudence Gibert, Hervé Blanchon 等 2022-07-17T21:18:03Z 置信度 0.78 cs.CL
• Generative AI for drug discovery and protein design: the next frontier in AI-driven molecular science
  Generative artificial intelligence (AI) has emerged as a disruptive paradigm in molecular science, enabling algorithmic navigation and construction of chemical and proteomic spaces through data-driven modeling. This review systematically delineates the theoret…
  crossref Uddalak Das 2025-07-02T04:05:22Z 置信度 0.70
• AI TOOLS FOR DRUG DISCOVERY
  The pharmaceutical industry is experiencing a revolutionary transformation through the integration of artificial intelligence (AI) and machine learning (ML) technologies in drug discovery and development processes. This comprehensive review examines the curren…
  crossref Rajendra K. Jain, Shailendra Yadav 2026-03-03T09:58:09Z 置信度 0.70
• Organoid-AI platforms need integrated governance in drug discovery.
  Organoid-AI platforms are becoming decision systems in drug discovery, not just combined tools. They shape compound prioritisation, toxicity assessment, and programme progression. Yet governance often validates the biological model and the computational model …
  europepmc 2026 置信度 0.80
• Harnessing AI in drug discovery without losing patent protection
  crossref Matt Hervey 2020-03-20T12:34:29Z 置信度 0.70
• AI IN DRUG DISCOVERY
  Drug discovery is a multifaceted and resource-intensive process traditionally marked by long development timelines, high financial costs, and significant attrition rates. The integration of Artificial Intelligence (AI) has introduced transformative opportuniti…
  crossref Dr. Pritam Juvatkar, Dr. Chaitrali Bidikar, Aditi Arun Mhatre 2026-03-03T09:58:09Z 置信度 0.70
• Pharma’s Bio-AI revolution
  Drug development has become unbearably slow and expensive. A key underlying problem is the clinical prediction challenge: the inability to predict which drug candidates will be safe in the human body and for whom. Recently, a dramatic regulatory change has rem…
  crossref Isaac Bentwich 2023-02-02T04:50:53Z 置信度 0.70
• Trustworthy and explainable AI for drug discovery.
  1. Major biopharmaceutical companies everywhere are pursuing major AI initiatives, with much of these efforts directed towards creating and curating FAIR datasets to train and validate predictive A...
  europepmc 2026 置信度 0.80
• Artificial intelligence (AI) and alternative variables (AV) in drug discovery: A promising alliance
  crossref Celerino Abad-Zapatero 2024-04-08T22:26:15Z 置信度 0.70
• AI in drug discovery: A wake-up call
  crossref Fabio Urbina, Filippa Lentzos, Cédric Invernizzi, Sean Ekins 2022-10-18T12:28:29Z 置信度 0.70
• Generative AI in pharmaceutical R&D: From large language models to AI agents to regulation
  Generative AI (GenAI) is reshaping pharmaceutical R&D, offering transformative potential across research and development. Applications of GenAI include scientific insight generation, mining large biological datasets to study diseases, molecule design, clinical…
  crossref 2026-01-08T16:56:33Z 置信度 0.70