@inproceedings{
plainer2023diffdockpocket,
title={DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility},
author={Michael Plainer and Marcella Toth and Simon Dobers and Hannes Stark and Gabriele Corso and C{\'e}line Marquet and Regina Barzilay},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=5Wt7qNRc5u}
}
When a small molecule binds to a protein, the 3D structure of the protein and its function change. Understanding this process, called molecular docking, can be crucial in areas such as drug design. Recent learning-based attempts have shown promising results at this task, yet lack features that traditional approaches support. In this work, we close this gap by proposing DiffDock-Pocket, a diffusion-based docking algorithm that is conditioned on a binding target to predict ligand poses only in a specific binding pocket. On top of this, our model supports receptor flexibility and predicts the position of sidechains close to the binding site. Empirically, we improve the state-of-the-art in site-specific-docking on the PDBBind benchmark. Especially when using in-silico generated structures, we achieve more than twice the performance of current methods while being more than 20 times faster than other flexible approaches. Although the model was not trained for cross-docking to different structures, it yields competitive results in this task.
DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility
Michael Plainer Marcella Toth Simon Dobers Hannes Stark Gabriele Corso Céline Marquet Regina Barzilay