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arXiv · 2307.12090

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

Michael Brocidiacono·Konstantin I. Popov·David Ryan Koes·Alexander Tropsha
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Abstract

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.

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Michael Brocidiacono, Konstantin I. Popov, David Ryan Koes, Alexander Tropsha. 2023-07-22. PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking. https://arxiv.org/abs/2307.12090

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