We would like to maintain a list of resources which aim to solve molecular docking and other closely related tasks.
We will update this repository regularly. 😎
If you want to add related works to this repository, please feel free to contact me via [email protected].
Welcome to contribute to this repository! 👏
Table of Contents 👈 click here to unfold the outlines
- Crampon, Kevin, et al. "Machine-learning methods for ligand–protein molecular docking." Drug discovery today (2021). [Paper]
- Harmalkar, Ameya, and Jeffrey J. Gray. "Advances to tackle backbone flexibility in protein docking." Current opinion in structural biology 67 (2021): 178-186. [Paper]
- PDBBind
- Structural Antibody Database (SAbDab)
- Database of Interacting Protein Structures (DIPS)
- ATTRACT
- HDOCK
- CLUSPRO
- PATCHDOCK
- Corso, Gabriele, et al. "DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking." arXiv preprint arXiv:2210.01776 (2022). [Paper][Code]
- Zhang, Yangtian, et al. "E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking." arXiv preprint arXiv:2210.06069 (2022). [Paper]
- Lu, Wei, et al. "TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction." Advances in Neural Information Processing Systems. 2022.[Paper][Code]
- Stärk, Hannes, et al. "Equibind: Geometric deep learning for drug binding structure prediction." International Conference on Machine Learning. PMLR, 2022. [Paper][Code]
- Ganea, Octavian-Eugen, et al. "Independent se (3)-equivariant models for end-to-end rigid protein docking." International Conference on Learning Representations (2022). [Paper][Code]
- Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., & Ma, J. Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures. In Advances in Neural Information Processing Systems. [Paper][Code]
- Jin, Wengong, Regina Barzilay, and Tommi Jaakkola. "Antibody-antigen docking and design via hierarchical structure refinement." International Conference on Machine Learning. PMLR, 2022. [Paper][Code]
- Fu, Xiang, et al. "Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning." arXiv preprint arXiv:2204.10348 (2022). [Paper][Code]
- Freyr, et al. "Fast end-to-end learning on protein surfaces." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. [Paper]
- Gainza, Pablo, et al. "Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning." Nature Methods 17.2 (2020): 184-192. [Paper][Code]