Awesome papers related to generative molecular modeling and design.

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• [Elsevier 2022] Deep learning approaches for de novo drug design: An overview [Paper]

• [Science 2024] Generalized biomolecular modeling and design with RoseTTAFold All-Atom [Paper][Code]

• [ICLR 2023] Conditional Antibody Design as 3D Equivariant Graph Translation [Paper][Code]
• [ICLR 2023] Protein Sequence and Structure Co-Design with Equivariant Translation [Paper]
• [ICLR MLDD 2023 & Nature CommsChem] Geometry-Complete Diffusion for 3D Molecule Generation and Optimization [Paper][Code]
• [ICLR MLDD 2023] EigenFold: Generative Protein Structure Prediction with Diffusion Models [Paper][Code]
• [ICLR MLDD 2023] DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models [Paper][Code]
  
  
• [ICML 2023] SE(3) diffusion model with application to protein backbone generation [Paper][Code]
• [ICML 2023] Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds [Paper][Code]
• [ICML WCB 2023] Multi-State RNA Design with Geometric Multi-Graph Neural Networks [Paper][Code]
  
  
• [NeurIPS 2023] Graph Denoising Diffusion for Inverse Protein Folding [Paper][Code]
• [NeurIPS MLSB 2023] Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion [Paper][Code]
• [NeurIPS MLSB 2023] Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design [Paper][Code]
• [NeurIPS MLSB 2023] AlphaFold Meets Flow Matching for Generating Protein Ensembles [Paper]
• [NeurIPS MLSB 2023] Fast protein backbone generation with SE(3) flow matching [Paper][Code]
  
  
• [ACS 2023] SILVR: Guided Diffusion for Molecule Generation [Paper][Code]
  
  
• [arXiv 2023] Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation [Paper][Code]
• [arXiv 2023] Domain-Agnostic Molecular Generation with Self-feedback [Paper][Code]
• [arXiv 2023] Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models [Paper][Code]
• [arXiv 2023] SE(3)-Stochastic Flow Matching for Protein Backbone Generation [Paper][Code]
• [bioRxiv 2023] Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design [Paper]

• [ICLR 2022] Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design [Paper][Code]
• [ICLR 2022] GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation [Paper][Code]
• [ICLR 2022] Energy-Inspired Molecular Conformation Optimization [Paper][Code]
• [ICLR 2022] Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking [Paper][Code]
  
  
• [ICML 2022] Generating 3D Molecules for Target Protein Binding [Paper][Code]
• [ICML 2022] Equivariant Diffusion for Molecule Generation in 3D [Paper][Code]
• [ICML 2022] LIMO: Latent Inceptionism for Targeted Molecule Generation [Paper][Code]
• [ICML 2022] Antibody-Antigen Docking and Design via Hierarchical Structure Refinement [Paper][Code]
• [ICML 2022] EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction [Paper][Code]
  
  
• [NeurIPS 2022] Antigen-specific antibody design and optimization with diffusion-based generative models [Paper][Code]
  
  
• [PLOS 2022] Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation [Paper][Code]
  
  
• [arXiv 2022] Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models [Paper]
• [arXiv 2022] Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem [Paper]
• [arXiv 2022] EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures [Paper][Code]
• [arXiv 2022] AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation [Paper]
• [arXiv 2022] ProGen2: Exploring the Boundaries of Protein Language Models [Paper][Code]
• [arXiv 2022] DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking [Paper][Code]
• [arXiv 2022] Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models [Paper]
• [arXiv 2022] Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design [Paper][Code]
  
  
• [bioRxiv 2022] Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models [Paper]
• [bioRxiv 2022] Learning inverse folding from millions of predicted structures [Paper][Code]
• [bioRxiv 2022] Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness [Paper]

• [NeurIPS 2021] A 3D Generative Model for Structure-Based Drug Design [Paper][Code]

• [NeurIPS 2020] Barking up the right tree: an approach to search over molecule synthesis DAGs [Paper][Code]
• [NeurIPS 2020 (MLCB)] ProGen: Language Modeling for Protein Generation [Paper][Code]

• [NeurIPS 2019] Generative models for graph-based protein design [Paper][Code]
  
  
• [Journal of Chemical Information and Modeling 2019] GuacaMol: Benchmarking Models for de Novo Molecular Design [Paper][Code]

• [ACS Cental Science 2017] Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks [Paper]