ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation

Part of Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Main Conference Track

Bibtex Paper

Authors

Majdi Hassan, Nikhil Shenoy, Jungyoon Lee, Hannes Stärk, Stephan Thaler, Dominique Beaini

Abstract

Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models thatdiffuse over conformer fields, or use computationally expensive methods to gen-erate initial structures and diffuse over torsion angles. In this work, we introduceEquivariant Transformer Flow (ET-Flow). We showcase that a well-designedflow matching approach with equivariance and harmonic prior alleviates the needfor complex internal geometry calculations and large architectures, contrary tothe prevailing methods in the field. Our approach results in a straightforwardand scalable method that directly operates on all-atom coordinates with minimalassumptions. With the advantages of equivariance and flow matching, ET-Flowsignificantly increases the precision and physical validity of the generated con-formers, while being a lighter model and faster at inference. Code is availablehttps://github.com/shenoynikhil/ETFlow.