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

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

Keywords: Flow Matching, molecular conformers, equivariant graph networks

TL;DR: We leverage flow matching and equivariant graph networks to state-of-the-art performance on generating conformers.

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 that
diffuse over conformer fields, or use computationally expensive methods to gen-
erate initial structures and diffuse over torsion angles. In this work, we introduce
Equivariant Transformer Flow (ET-Flow). We showcase that a well-designed
flow matching approach with equivariance and harmonic prior alleviates the need
for complex internal geometry calculations and large architectures, contrary to
the prevailing methods in the field. Our approach results in a straightforward
and scalable method that directly operates on all-atom coordinates with minimal
assumptions. With the advantages of equivariance and flow matching, ET-Flow
significantly increases the precision and physical validity of the generated con-
formers, while being a lighter model and faster at inference. Code is available
https://github.com/shenoynikhil/ETFlow.

Primary Area: Machine learning for physical sciences (for example: climate, physics)

Submission Number: 19872