Link to the survey paper: Geometric Deep Learning for Structure-Based Drug Design: A Survey
This repository contains a list of papers on the Structure-based Drug Design; we categorize them based on their detailed tasks and methods. Specifically, we focus our scope on tasks related to the protein-ligand binding interface, including binding site prediction, binding pose generation, de novo ligand generation, linker design, protein pocket generation, and binding affinity prediction.
We will try to make this list updated. If you found any error or any missed paper, please don't hesitate to open an issue or pull request.
- Binding site prediction
- Binding pose generation
- Binding affinity prediction
- De novo ligand generation
- Linker design
- Protein Pocket Generation
Surface-VQMAE: Vector-Quantized Masked Auto-Encoders on Molecular Surfaces
Wu, Fang et al.
International Conference on Machine Learning, 2024
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces
Krapp, Lucien F., et al
Nature Communications 14 (2023): 2175
Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network
Meller, Artur, et al
Nature Communications 14 (2023): 1177
EquiPocket: an E (3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
Zhang, Yang, et al
arXiv preprint arXiv:2302.12177 (2023)
NodeCoder: a graph-based machine learning platform to predict active sites of modeled protein structures
Abdollahi, Nasim, et al
arXiv preprint arXiv:2302.03590 (2023)
DSDP: A Blind Docking Strategy Accelerated by GPUs
Huang, Yupeng et al
Journal of chemical information and modeling (2023)
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction
Tubiana, Jérôme, Dina Schneidman-Duhovny, and Haim J. Wolfson
Nature Methods 19.6 (2022): 730-739
Fast end-to-end learning on protein surfaces
Sverrisson, Freyr, et al
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021
PUResNet: prediction of protein-ligand binding sites using deep residual neural network
Qiao, Zhuoran et al
arXiv preprint arXiv:2209.15171 (2021)
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Gainza, Pablo et al
Nature Methods 17 (2019): 184-192
Protein interface prediction using graph convolutional networks
Fout, Alex, et al
Advances in neural information processing systems 30 (2017)
DeepSite: protein-binding site predictor using 3D-convolutional neural networks
Jiménez, José, et al
Bioinformatics 33.19 (2017): 3036-3042
DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model
Lu, Wei et al
Nature Communications 15 (2024)
PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking
Zhang, Runze et al
bioRxiv (2024): n. pag
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Huang, Yufei et al
Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms
Jing, Bowen et al
International Conference on Learning Representations, 2024
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
Stärk, Hannes et al
International Conference on Machine Learning, 2024
Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction
Alcaide, Eric et al
ArXiv abs/2405.11769 (2024)
FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation
Gao, Kaiyuan et al
ArXiv abs/2403.20261 (2024): n. pag
Deep Confident Steps to New Pockets: Strategies for Docking Generalization
Corso, Gabriele et al
ArXiv (2024): n. pag
Chai-1: Decoding the molecular interactions of life
Boitreaud, Jacques et al
bioRxiv (2024): n. pag
Accurate structure prediction of biomolecular interactions with AlphaFold 3
Abramson, Josh et al
Nature 630 (2024): 493 - 500
Deep learning model for efficient protein–ligand docking with implicit side-chain flexibility
Masters, Matthew R., et al
Journal of Chemical Information and Modeling 63.6 (2023): 1695-1707
End-to-end protein–ligand complex structure generation with diffusion-based generative models
Nakata, Shuya, Yoshiharu Mori, and Shigenori Tanaka
BMC bioinformatics 24.1 (2023): 1-18
Diffdock: Diffusion steps, twists, and turns for molecular docking
Corso, Gabriele, et al
International Conference on Learning Representations. 2023
Uni-Mol: A Universal 3D Molecular Representation Learning Framework
Zhou, Gengmo et al
International Conference on Learning Representations (2023)
E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking
Zhang, Yangtian, et al
International Conference on Learning Representations. 2023
A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function
Wang, Zechen, et al
Briefings in Bioinformatics 24.1 (2023): bbac520
Protein-Ligand Complex Generator & Drug Screening via Tiered Tensor Transform
Mailoa, Jonathan P., et al
arXiv preprint arXiv:2301.00984 (2023)
DSDP: A Blind Docking Strategy Accelerated by GPUs
Huang, Yupeng et al
Journal of chemical information and modeling (2023)
Equivariant Flexible Modeling of the Protein-Ligand Binding Pose with Geometric Deep Learning
Dong, Tiejun et al
Journal of chemical theory and computation (2023)
DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility
Plainer, Michael, et al
Advances in Neural Information Processing Systems, Workshop. 2023
Efficient and accurate large library ligand docking with KarmaDock
Zhang, Xujun et al
Nature Computational Science 3 (2023): 789 - 804
CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training
Cai, Heng et al
Chemical Science 15 (2023): 1449 - 1471
FABind: Fast and Accurate Protein-Ligand Binding
Pei, Qizhi et al
Advances in Neural Information Processing Systems. 2023
Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking
Yan, Jiaxian et al
ArXiv abs/2311.18574 (2023)
SurfDock is a Surface-Informed Diffusion Generative Model for Reliable and Accurate Protein-ligand Complex Prediction
Cao, Duanhua et al
bioRxiv (2023): n. pag
DiffBindFR: An SE(3) Equivariant Network for Flexible Protein-Ligand Docking
Zhu, Jintao et al
Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models
Liu, Lihang et al
ArXiv abs/2310.13913 (2023): n. pag
Structure prediction of protein-ligand complexes from sequence information with Umol
Bryant, Patrick et al
Nature Communications 15 (2023): n. pag
Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom
Krishna, Rohith et al
bioRxiv (2023): n. pag
Equibind: Geometric deep learning for drug binding structure prediction
Stärk, Hannes, et al
International Conference on Machine Learning. PMLR, 2022
TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction
Lu, Wei, et al
Advances in Neural Information Processing Systems. 2022
State-specific protein-ligand complex structure prediction with a multi-scale deep generative model
Qiao, Zhuoran et al
arXiv preprint arXiv:2209.15171 (2021)
A geometric deep learning approach to predict binding conformations of bioactive molecules
Méndez-Lucio, Oscar, et al
Nature Machine Intelligence 3.12 (2021)
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
Zheng, Kangjie et al
International Conference on Machine Learning, 2024
Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning
Kong, Xiangzhe et al
International Conference on Machine Learning, 2024
Protein-ligand binding representation learning from fine-grained interactions
Feng, Shikun et al
International Conference on Learning Representations, 2024
Geometric Interaction Graph Neural Network for Predicting Protein–Ligand Binding Affinities from 3D Structures (GIGN)
Yang, Ziduo, et al
The Journal of Physical Chemistry Letters 14.8 (2023): 2020-2033
Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction
Yan, Jiaxian, et al
arXiv preprint arXiv:2306.04886 (2023)
Improving drug-target affinity prediction via feature fusion and knowledge distillation
Lu, Ruiqiang, et al
Briefings in Bioinformatics 24.3 (2023)
GIANT: Protein-Ligand Binding Affinity Prediction via Geometry-aware Interactive Graph Neural Network
Li, Shuangli et al
IEEE Transactions on Knowledge and Data Engineering (2023): n. pag
PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions
Moon, Seokhyun, et al
Chemical Science 13.13 (2022): 3661-3673
Protein-ligand interaction graphs: Learning from ligand-shaped 3d interaction graphs to improve binding affinity prediction
Moesser, Marc A., et al
bioRxiv (2022): 2022-03
Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes
Zhang, Shuo, Yang Liu, and Lei Xie
arXiv preprint arXiv:2206.02789 (2022)
Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity
Li, Shuangli, et al
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021
Multi-scale representation learning on proteins
Somnath, Vignesh Ram, Charlotte Bunne, and Andreas Krause
Advances in Neural Information Processing Systems 34 (2021): 25244-25255
Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions
Jiang, Dejun, et al
Journal of medicinal chemistry 64.24 (2021): 18209-18232
Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures
Hermosilla, Pedro, et al
International Conference on Learning Representations. 2021
Improved protein–ligand binding affinity prediction with structure-based deep fusion inference
Jones, Derek, et al
Journal of chemical information and modeling 61.4 (2021)
RosENet: improving binding affinity prediction by leveraging molecular mechanics energies with an ensemble of 3D convolutional neural networks
Hassan-Harrirou, Hussein, Ce Zhang, and Thomas Lemmin
Journal of chemical information and modeling 60.6 (2020): 2791-2802
DeepAtom: A framework for protein-ligand binding affinity prediction
Li, Yanjun, et al
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction
Stepniewska-Dziubinska, Marta M., Piotr Zielenkiewicz, and Pawel Siedlecki
Bioinformatics 34.21 (2018): 3666-3674
Protein-Ligand Interaction Prior for Binding-aware 3D Molecule Diffusion Models
Huang, Zhilin et al
International Conference on Learning Representations, 2024
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization
Zhou, Xiangxin et al
International Conference on Learning Representations, 2024
Interaction-based Retrieval-augmented Diffusion Models for Protein-specific 3D Molecule Generation
Huang, Zhilin et al
International Conference on Machine Learning, 2024
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
Adams, Keir, and Connor W. Coley
International Conference on Learning Representations. 2023
Prefixmol: Target-and chemistry-aware molecule design via prefix embedding
Gao, Zhangyang, et al
arXiv preprint arXiv:2302.07120 (2023)
Molecule generation for target protein binding with structural motifs
Zhang, Zaixi, et al
International Conference on Learning Representations. 2023
Learning Subpocket Prototypes for Generalizable Structure-based Drug Design
Zhang, Zaixi, and Qi Liu
International Conference on Machine Learning. PMLR, 2023
Lingo3DMol: Generation of a Pocket-based 3D Molecule using a Language Model
Wang, Lvwei, et al
arXiv preprint arXiv:2305.10133 (2023)
An Equivariant Generative Framework for Molecular Graph-Structure Co-Design
Zhang, Zaixi, et al
bioRxiv (2023): 2023-04
GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow
Sun, Fang, et al
arXiv preprint arXiv:2304.12825 (2023)
Structure-Based Drug Design via Semi-Equivariant Conditional Normalizing Flows
Rozenberg, Eyal, Ehud Rivlin, and Daniel Freedman
ICLR 2023-Machine Learning for Drug Discovery workshop. 2023
3d equivariant diffusion for target-aware molecule generation and affinity prediction
Guan, Jiaqi, et al
International Conference on Learning Representations. 2023
ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling
Zhang, Odin et al
Nature Machine Intelligence 5 (2023): 1020 - 1030
Learning on topological surface and geometric structure for 3D molecular generation
Zhang, Odin et al
Nature Computational Science 3 (2023): 849 - 859
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
Guan, Jiaqi et al
International Conference on Machine Learning (2023)
Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
Lin, Haitao et al
ArXiv abs/2306.13769 (2023): n. pag
Reinforced genetic algorithm for structure-based drug design
Fu, Tianfan, et al
Advances in Neural Information Processing Systems 35 (2022): 12325-12338
Relation: A deep generative model for structure-based de novo drug design
Wang, Mingyang, et al
Journal of Medicinal Chemistry 65.13 (2022): 9478-9492
Synthesis-driven design of 3D molecules for structure-based drug discovery using geometric transformers
Li, Yibo, Jianfeng Pei, and Luhua Lai
arXiv preprint arXiv:2301.00167 (2022)
Pocket2mol: Efficient molecular sampling based on 3d protein pockets
Peng, Xingang, et al
International Conference on Machine Learning. PMLR, 2022
Zero-Shot 3D Drug Design by Sketching and Generating
Long, Siyu, et al
arXiv preprint arXiv:2209.13865 (2022)
Generating 3d molecules for target protein binding
Liu, Meng, et al
International Conference on Machine Learning. PMLR, 2022
Structure-based drug design with equivariant diffusion models
Schneuing, Arne, et al
arXiv preprint arXiv:2210.13695 (2022)
DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
Lin, Haitao, et al
arXiv preprint arXiv:2211.11214 (2022)
Structure-based de novo drug design using 3D deep generative models
Li, Yibo, Jianfeng Pei, and Luhua Lai
Chemical science 12.41 (2021): 13664-13675
A 3D generative model for structure-based drug design
Luo, Shitong, et al
Advances in Neural Information Processing Systems 34 (2021): 6229-6239
AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization
Spiegel, Jacob O., and Jacob D. Durrant
Journal of cheminformatics 12.1 (2020): 1-16
Generating 3d molecular structures conditional on a receptor binding site with deep generative models
Masuda, Tomohide, Matthew Ragoza, and David Ryan Koes
arXiv preprint arXiv:2010.14442 (2020)
3D Based Generative PROTAC Linker Design with Reinforcement Learning
Chen, Hongming
LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion
Guan, Jiaqi, et al
Advances in Neural Information Processing Systems, 2023
3dlinker: An e (3) equivariant variational autoencoder for molecular linker design
Huang, Yinan, et al
International Conference on Machine Learning. PMLR, 2022
Equivariant 3d-conditional diffusion models for molecular linker design
Igashov, Ilia, et al
arXiv preprint arXiv:2210.05274 (2022)
Deep generative models for 3D linker design
Imrie, Fergus, et al
Journal of chemical information and modeling 60.4 (2020)
Generalized Protein Pocket Generation with Prior-Informed Flow Matching
Zhang, Zaixi, Zitnik, Marinka, and Liu, Qi
Advances in Neural Information Processing Systems, 2024
Efficient Generation of Protein Pockets with PocketGen
Zhang, Zaixi, Shen, Wan Xiang, Liu, Qi, and Zitnik, Marinka
Nature Machine Intelligence, 2024
De Novo Design of Protein Structure and Function with RFdiffusion
Watson, Joseph L., Juergens, David, Bennett, Nathaniel R., Trippe, Brian L., Yim, Jason, Eisenach, Helen E., Ahern, Woody, Borst, Andrew J., Ragotte, Robert J., Milles, Lukas F., et al.
Nature, 2023, Volume 620, Issue 7976, Pages 1089–1100
De Novo Design of Protein Structure and Function with RFdiffusion
Watson, Joseph L., Juergens, David, Bennett, Nathaniel R., Trippe, Brian L., Yim, Jason, Eisenach, Helen E., Ahern, Woody, Borst, Andrew J., Ragotte, Robert J., Milles, Lukas F., et al.
Nature, 2023
Full-Atom Protein Pocket Design via Iterative Refinement
Zhang, Zaixi, Lu, Zepu, Zhongkai, Hao, Zitnik, Marinka, and Liu, Qi
Advances in Neural Information Processing Systems, 2023, Volume 36, Pages 16816–16836
If you find this repo to be useful, please cite our paper. Thank you!
@article{zhang2023systematic,
title={A systematic survey in geometric deep learning for structure-based drug design},
author={Zhang, Zaixi and Yan, Jiaxian and Liu, Qi and Chen, Enhong and Zitnik, Marinka},
journal={arXiv preprint arXiv:2306.11768},
year={2023}
}