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]

• [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]
  
  
• [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]