Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915529998991360 |
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| author | Bergues, Noémie Carré, Arthur Join-Lambert, Paul Hoffmann, Brice Blondel, Arnaud Tajmouati, Hamza |
| author_facet | Bergues, Noémie Carré, Arthur Join-Lambert, Paul Hoffmann, Brice Blondel, Arnaud Tajmouati, Hamza |
| contents | Predicting the 3D conformation of small molecules within protein binding sites is a key challenge in drug design. When a crystallized reference ligand (template) is available, it provides geometric priors that can guide 3D pose prediction. We present a two-stage method for ligand conformation generation guided by such templates. In the first stage, we introduce a molecular alignment approach based on flow-matching to generate 3D coordinates for the ligand, using the template structure as a reference. In the second stage, a differentiable pose optimization procedure refines this conformation based on shape and pharmacophore similarities, internal energy, and, optionally, the protein binding pocket. We introduce a new benchmark of ligand pairs co-crystallized with the same target to evaluate our approach and show that it outperforms standard docking tools and open-access alignment methods, especially in cases involving low similarity to the template or high ligand flexibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization Bergues, Noémie Carré, Arthur Join-Lambert, Paul Hoffmann, Brice Blondel, Arnaud Tajmouati, Hamza Biomolecules Machine Learning Predicting the 3D conformation of small molecules within protein binding sites is a key challenge in drug design. When a crystallized reference ligand (template) is available, it provides geometric priors that can guide 3D pose prediction. We present a two-stage method for ligand conformation generation guided by such templates. In the first stage, we introduce a molecular alignment approach based on flow-matching to generate 3D coordinates for the ligand, using the template structure as a reference. In the second stage, a differentiable pose optimization procedure refines this conformation based on shape and pharmacophore similarities, internal energy, and, optionally, the protein binding pocket. We introduce a new benchmark of ligand pairs co-crystallized with the same target to evaluate our approach and show that it outperforms standard docking tools and open-access alignment methods, especially in cases involving low similarity to the template or high ligand flexibility. |
| title | Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2506.06305 |