Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design. Recent machine learning methods have formulated protein-small molecule docking as a generative problem with significant performance boosts over both traditional and deep learning baselines. In this work, we propose a similar approach for rigid protein-protein docking: DiffDock-PP is a diffusion generative model that learns to translate and rotate unbound protein structures into their bound conformations. We achieve state-of-the-art performance on DIPS with a median C-RMSD of 4.85, outperforming all considered baselines. Additionally, DiffDock-PP is faster than all search-based methods and generates reliable confidence estimates for its predictions. Our code is publicly available at https://github.com/ketatam/DiffDock-PP
@article{arxiv.2304.03889,
title = {DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models},
author = {Mohamed Amine Ketata and Cedrik Laue and Ruslan Mammadov and Hannes Stärk and Menghua Wu and Gabriele Corso and Céline Marquet and Regina Barzilay and Tommi S. Jaakkola},
journal= {arXiv preprint arXiv:2304.03889},
year = {2023}
}
Comments
ICLR Machine Learning for Drug Discovery (MLDD) Workshop 2023