Molecular docking aims to predict the 3D pose of a small molecule in a protein binding site. Traditional docking methods predict ligand poses by minimizing a physics-inspired scoring function. Recently, a diffusion model has been proposed that iteratively refines a ligand pose. We combine these two approaches by training a pose scoring function in a diffusion-inspired manner. In our method, PLANTAIN, a neural network is used to develop a very fast pose scoring function. We parameterize a simple scoring function on the fly and use L-BFGS minimization to optimize an initially random ligand pose. Using rigorous benchmarking practices, we demonstrate that our method achieves state-of-the-art performance while running ten times faster than the next-best method. We release PLANTAIN publicly and hope that it improves the utility of virtual screening workflows.
@article{arxiv.2307.12090,
title = {PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking},
author = {Michael Brocidiacono and Konstantin I. Popov and David Ryan Koes and Alexander Tropsha},
journal= {arXiv preprint arXiv:2307.12090},
year = {2023}
}
Comments
Camera-ready submission to ICML CompBio workshop. 5 pages and 1 figure