DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
arXiv:2210.01776
Abstract
Predicting the binding structure of a small molecule ligand to a protein -- a task known as molecular docking -- is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods but have yet to offer substantial improvements in accuracy. We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses. To do so, we map this manifold to the product space of the degrees of freedom (translational, rotational, and torsional) involved in docking and develop an efficient diffusion process on this space. Empirically, DiffDock obtains a 38% top-1 success rate (RMSD<2A) on PDBBind, significantly outperforming the previous state-of-the-art of traditional docking (23%) and deep learning (20%) methods. Moreover, while previous methods are not able to dock on computationally folded structures (maximum accuracy 10.4%), DiffDock maintains significantly higher precision (21.7%). Finally, DiffDock has fast inference times and provides confidence estimates with high selective accuracy.
International Conference on Learning Representations (ICLR 2023)
Cited by in corpus (8)
- Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
- ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation
- Machine learning-assisted search for novel coagulants: when machine learning can be efficient even if data availability is low
- Reflection-Equivariant Diffusion for 3D Structure Determination from Isotopologue Rotational Spectra in Natural Abundance
- Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte Carlo
- Unraveling the Potential of Diffusion Models in Small Molecule Generation
- Physics-informed generative model for drug-like molecule conformers
- Zero Shot Molecular Generation via Similarity Kernels