Key papers

• Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets (2022)
  Xingang Peng et al.
  10.00
• Metagenomic-scale analysis of the predicted protein structure universe (2026)
  Jingi Yeo et al.
  9.98
• Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022)
  Tao Shen et al.
  9.35
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems (2023)
  Xuan Zhang et al.
  9.29
• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications (2025)
  Kai Zhu et al.
  8.98
• Evaluating zero‐shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN (2026)
  Mario Garcia et al.
  8.23
• Unified modeling of 3D molecular generation via atomic interactions with PocketXMol. (2026)
  Xingang Peng et al.
  8.23
• Protenix-v1: Toward High-Accuracy Open-Source Biomolecular Structure Prediction (2026)
  Yuxuan Zhang et al.
  8.23
• Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design (2025)
  Tong Chen et al.
  8.18
• SE(3)-Equivariant Ternary Complex Prediction Towards Target Protein Degradation (2025)
  Fanglei Xue et al.
  8.12
• Multi-domain Distribution Learning for De Novo Drug Design (2025)
  Arne Schneuing et al.
  7.86
• Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry (2025)
  Tsz Wai Ko et al.
  7.71
• Machine Learning and Data-Driven Methods in Computational Surface and Interface Science (2025)
  Lukas H\"ormann et al.
  7.71
• HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data (2025)
  Hiren Madhu et al.
  7.59
• Amortized Sampling with Transferable Normalizing Flows (2025)
  Charlie B. Tan et al.
  7.41
• MDCrow: Automating Molecular Dynamics Workflows with Large Language Models (2025)
  Quintina Campbell et al.
  7.39
• Mathematics of Data Science (2026)
  Afonso S. Bandeira et al.
  7.34
• 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
• Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation (2025)
  Sophia Tang et al.
  7.13
• Machine learning, docking, or physics for structure prediction of ligand-induced ternary complexes. (2026)
  Riccardo Solazzo et al.
  7.13
• FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction (2024)
  Alex Morehead and Jianlin Cheng
  7.08
• Systematic Analysis of Biomolecular Conformational Ensembles with PENSA (2022)
  Martin V\"ogele et al.
  7.01
• Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization (2026)
  Minhuan Li et al.
  6.91
• FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation (2025)
  Julian Cremer et al.
  6.83
• UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design (2025)
  Xiangzhe Kong 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
• Physics-Informed Machine Learning in Biomedical Science and Engineering (2025)
  Nazanin Ahmadi et al.
  6.75
• MatterChat: A Multi-Modal LLM for Material Science (2025)
  Yingheng Tang et al.
  6.72
• BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery (2024)
  Peter St. John et al.
  6.67
• Autofocused oracles for model-based design (2020)
  Clara Fannjiang and Jennifer Listgarten
  6.66
• Understanding protein function with a multimodal retrieval-augmented foundation model (2025)
  Timothy Fei Truong Jr et al.
  6.64
• Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review (2025)
  Johnny Peng et al.
  6.53
• CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM (2024)
  Minkyu Jeon et al.
  6.50
• QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials (2025)
  Francesc Saban\'es Zariquiey et al.
  6.47
• BioReason-Pro: Advancing Protein Function Prediction with Multimodal Biological Reasoning (2026)
  Adibvafa Fallahpour et al.
  6.40
• Protein Solubility Prediction Using Fused Graph Convolutional Networks and Improved Attention Networks with AlphaFold3-Derived Features (2026)
  Guanjie Song et al.
  6.40
• When cryo-EM modeling meets structure prediction (2026)
  Xiaogen Zhou et al.
  6.40
• RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design (2024)
  Rishabh Anand et al.
  6.39
• CrystalBoltz: End-to-End Protein Structure Determination via Experiment-Guided Diffusion for X-Ray Crystallography (2026)
  Minseo Kim et al.
  6.35
• Inference-time optimization for experiment-grounded protein ensemble generation (2026)
  Advaith Maddipatla et al.
  6.18
• Sparks: Multi-Agent Artificial Intelligence Model Discovers Protein Design Principles (2025)
  Alireza Ghafarollahi and Markus J. Buehler
  6.17
• Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies (2025)
  Moritz Bensberg et al.
  6.12
• Lambda-ABF-OPES: Faster Convergence with High Accuracy in Alchemical Free Energy Calculations (2025)
  Narjes Ansari et al.
  6.06
• DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules (2025)
  Junjie Xu et al.
  6.01
• Prospects for NMR Spectral Prediction on Fault-Tolerant Quantum Computers (2024)
  Justin E. Elenewski and Christina M. Camara and Amir Kalev
  5.91
• General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design (2024)
  Yue Jian et al.
  5.91
• Flexibility-Conditioned Protein Structure Design with Flow Matching (2025)
  Vsevolod Viliuga et al.
  5.82
• Boltz-ABFE: Free Energy Perturbation without Crystal Structures (2025)
  Stephan Thaler et al.
  5.82
• Universally Converging Representations of Matter Across Scientific Foundation Models (2025)
  Sathya Edamadaka et al.
  5.81
• PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion (2024)
  Sophia Tang et al.
  5.79
• Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design (2025)
  Xingyu Su et al.
  5.76
• All-atom inverse protein folding through discrete flow matching (2025)
  Kai Yi and Kiarash Jamali and Sjors H. W. Scheres
  5.76
• Molecular Fingerprints Are Strong Models for Peptide Function Prediction (2025)
  Jakub Adamczyk et al.
  5.73
• DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation (2025)
  Dan Luo et al.
  5.65
• Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing (2025)
  Zijie Qiu et al.
  5.65
• SciSciGPT: Advancing Human-AI Collaboration in the Science of Science (2025)
  Erzhuo Shao et al.
  5.59
• ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding (2024)
  Yijia Xiao et al.
  5.57
• AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance (2025)
  Tong Chen et al.
  5.57
• Learning conformational ensembles of proteins based on backbone geometry (2025)
  Nicolas Wolf et al.
  5.54