Paper

PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking

Popov, Konstantin I, Tropsha, Alexander, David Ryan Koes, Brocidiacono, Michael

2023

Overview

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.

• Publisher: Cornell University Library, arXiv.org
• Subject: Binding sites / Diffusion rate / Ligands / Molecular docking / Neural networks / Optimization / Plantains