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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.11759 |
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| _version_ | 1866913943536009216 |
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| author | Volokhova, Alexandra Ezzine, Léna Néhale Gaiński, Piotr Scimeca, Luca Bengio, Emmanuel Tossou, Prudencio Bengio, Yoshua Hernandez-Garcia, Alex |
| author_facet | Volokhova, Alexandra Ezzine, Léna Néhale Gaiński, Piotr Scimeca, Luca Bengio, Emmanuel Tossou, Prudencio Bengio, Yoshua Hernandez-Garcia, Alex |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Torsional-GFN: a conditional conformation generator for small molecules Volokhova, Alexandra Ezzine, Léna Néhale Gaiński, Piotr Scimeca, Luca Bengio, Emmanuel Tossou, Prudencio Bengio, Yoshua Hernandez-Garcia, Alex Machine Learning 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. |
| title | Torsional-GFN: a conditional conformation generator for small molecules |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.11759 |