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

• URL: http://arxiv.org/abs/2410.22388v1
• Date: Tue, 29 Oct 2024 16:44:10 GMT
• Title: ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation
• Authors: Majdi Hassan, Nikhil Shenoy, Jungyoon Lee, Hannes Stark, Stephan Thaler, Dominique Beaini,
• Abstract summary: We introduce Equivariant Transformer Flow (ET-Flow) to predict low-energy molecular conformations. Our approach results in a straightforward and scalable method that operates on all-atom coordinates with minimal assumptions. ET-Flow significantly increases the precision and physical validity of the generated conformers, while being a lighter model and faster at inference.
• Score: 3.4146914514730633
• License:
• 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 generate 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 conformers, while being a lighter model and faster at inference. Code is available https://github.com/shenoynikhil/ETFlow.

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