Torsional-GFN: a conditional conformation generator for small molecules

• URL: http://arxiv.org/abs/2507.11759v1
• Date: Tue, 15 Jul 2025 21:53:25 GMT
• Title: Torsional-GFN: a conditional conformation generator for small molecules
• Authors: Alexandra Volokhova, Léna Néhale Ezzine, Piotr Gaiński, Luca Scimeca, Emmanuel Bengio, Prudencio Tossou, Yoshua Bengio, Alex Hernandez-Garcia,
• Abstract summary: We introduce a conditional GFlowNet specifically designed to sample conformations of molecules proportionally to their Boltzmann distribution.<n>Our work presents a promising avenue for scaling the proposed approach to larger molecular systems.
• Score: 75.91029322687771
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating stable molecular conformations is crucial in several drug discovery applications, such as estimating the binding affinity of a molecule to a target. Recently, generative machine learning methods have emerged as a promising, more efficient method than molecular dynamics for sampling of conformations from the Boltzmann distribution. In this paper, we introduce Torsional-GFN, a conditional GFlowNet specifically designed to sample conformations of molecules proportionally to their Boltzmann distribution, using only a reward function as training signal. Conditioned on a molecular graph and its local structure (bond lengths and angles), Torsional-GFN samples rotations of its torsion angles. Our results demonstrate that Torsional-GFN is able to sample conformations approximately proportional to the Boltzmann distribution for multiple molecules with a single model, and allows for zero-shot generalization to unseen bond lengths and angles coming from the MD simulations for such molecules. Our work presents a promising avenue for scaling the proposed approach to larger molecular systems, achieving zero-shot generalization to unseen molecules, and including the generation of the local structure into the GFlowNet model.

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