Awesome Drug Discovery

Key papers

• AI-Driven Drug Discovery: A Comprehensive Review (2026)
  Garima Sharma
  17.37
• The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models (2025)
  Daniel S. Levine et al.
  11.16
• InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery (2026)
  Shiyang Feng et al.
  11.14
• BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology (2025)
  Ludovico Mitchener et al.
  10.71
• BERT Learns (and Teaches) Chemistry (2020)
  Josh Payne et al.
  10.07
• MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules (2023)
  D\'avid P\'eter Kov\'acs et al.
  10.02
• Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets (2022)
  Xingang Peng et al.
  10.00
• MassSpecGym: A benchmark for the discovery and identification of molecules (2024)
  Roman Bushuiev et al.
  9.94
• SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration (2024)
  Joseph M. Cavanagh et al.
  9.86
• Digital materials ecosystem: from databases to AI agents for autonomous discovery (2026)
  Di Zhang et al.
  9.81
• Towards an AI co-scientist (2025)
  Juraj Gottweis et al.
  9.77
• DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively (2025)
  Yixuan Weng et al.
  9.55
• UMA: A Family of Universal Models for Atoms (2025)
  Brandon M. Wood et al.
  9.52
• Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022)
  Tao Shen et al.
  9.35
• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications (2025)
  Kai Zhu et al.
  9.09
• Graph Neural Networks in Modern AI-aided Drug Discovery (2025)
  Odin Zhang et al.
  8.75
• Equivariant Neural Diffusion for Molecule Generation (2025)
  Fran\c{c}ois Cornet and Grigory Bartosh and Mikkel N. Schmidt and Christian A. Naesseth
  8.75
• Machine Learning for De Novo Molecular Generation: A Comprehensive Review. (2026)
  Yingjun Chen et al.
  8.34
• Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design (2025)
  Tong Chen et al.
  8.29
• The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search (2025)
  Yutaro Yamada et al.
  8.23
• Learning-Order Autoregressive Models with Application to Molecular Graph Generation (2025)
  Zhe Wang et al.
  8.18
• PharmAgents: Building a Virtual Pharma with Large Language Model Agents (2025)
  Bowen Gao et al.
  8.07
• Multi-domain Distribution Learning for De Novo Drug Design (2025)
  Arne Schneuing et al.
  7.97
• Antiviral drug discovery and development: challenges and future directions (2026)
  Shaoqing Du et al.
  7.84
• Artificial Intelligence in Drug Discovery: Integrative Advances From Data to Therapeutic Innovation (2026)
  M. Mehran et al.
  7.84
• Benchmarking Pretrained Molecular Embedding Models For Molecular Representation Learning (2025)
  Mateusz Praski et al.
  7.83
• AI-Powered Prediction of Nanoparticle Pharmacokinetics: A Multi-View Learning Approach (2025)
  Amirhossein Khakpour et al.
  7.82
• FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation (2025)
  Ian Dunn et al.
  7.68
• RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks (2024)
  Rafael Josip Peni\'c et al.
  7.62
• System of Agentic AI for the Discovery of Metal-Organic Frameworks (2025)
  Theo Jaffrelot Inizan et al.
  7.61
• Amortized Sampling with Transferable Normalizing Flows (2025)
  Charlie B. Tan et al.
  7.52
• SE(3)-Equivariant Ternary Complex Prediction Towards Target Protein Degradation (2025)
  Fanglei Xue et al.
  7.50
• Large Language Models to Accelerate Organic Chemistry Synthesis (2025)
  Yu Zhang et al.
  7.46
• Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization (2025)
  Jiajun Yu et al.
  7.40
• drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network (2024)
  Yoshitaka Inoue et al.
  7.38
• Computing solvation free energies of small molecules with experimental accuracy (2024)
  J. Harry Moore et al.
  7.38
• MDCrow: Automating Molecular Dynamics Workflows with Large Language Models (2025)
  Quintina Campbell et al.
  7.35
• AgenticSciML: Collaborative Multi-Agent Systems for Emergent Discovery in Scientific Machine Learning (2025)
  Qile Jiang et al.
  7.33
• A Text-guided Protein Design Framework (2023)
  Shengchao Liu et al.
  7.32
• Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs (2024)
  Daniil A. Boiko et al.
  7.30
• ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge (2025)
  Zihan Zhao et al.
  7.30
• Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation (2025)
  Sophia Tang et al.
  7.24
• Unified modeling of 3D molecular generation via atomic interactions with PocketXMol. (2026)
  Xingang Peng et al.
  7.24
• Self-Assembled Monolayers in p-i-n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning-Accelerated Material Discovery. (2026)
  Asmat Ullah et al.
  7.24
• Transformers in drug discovery: fine-tuning ChemBERTa for high-accuracy prediction of solubility, toxicity and binding affinity. (2026)
  S. Alagarsamy et al.
  7.24
• Multi-view biomedical foundation models for molecule-target and property prediction (2024)
  Parthasarathy Suryanarayanan et al.
  7.23
• Contextualizing biological perturbation experiments through language (2025)
  Menghua Wu et al.
  7.19
• Challenging reaction prediction models to generalize to novel chemistry (2025)
  John Bradshaw et al.
  7.13
• Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology (2025)
  Nazanin Ahmadi Daryakenari et al.
  7.13
• Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces (2025)
  Henry B. Moss et al.
  7.11
• FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction (2024)
  Alex Morehead and Jianlin Cheng
  7.08
• Does Hessian Data Improve the Performance of Machine Learning Potentials? (2025)
  Austin Rodriguez and Justin S. Smith and Jose L. Mendoza-Cortes
  7.07
• JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data (2024)
  Apurva Kalia et al.
  7.02
• Systematic Analysis of Biomolecular Conformational Ensembles with PENSA (2022)
  Martin V\"ogele et al.
  7.01
• BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments (2024)
  Yusuf Roohani et al.
  7.00
• FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation (2025)
  Julian Cremer et al.
  6.95
• UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design (2025)
  Xiangzhe Kong et al.
  6.89
• AlphaEvolve: A coding agent for scientific and algorithmic discovery (2025)
  Alexander Novikov et al.
  6.88
• Re-evaluating sample efficiency in de novo molecule generation (2022)
  Morgan Thomas et al.
  6.78
• PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences (2023)
  Martin Buttenschoen et al.
  6.77