Sesame: Opening the door to protein pockets
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916936985608192 |
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| author | Miñán, Raúl Perez-Lopez, Carles Iglesias, Javier Ciudad, Álvaro Molina, Alexis |
| author_facet | Miñán, Raúl Perez-Lopez, Carles Iglesias, Javier Ciudad, Álvaro Molina, Alexis |
| contents | Molecular docking is a cornerstone of drug discovery, relying on high-resolution ligand-bound structures to achieve accurate predictions. However, obtaining these structures is often costly and time-intensive, limiting their availability. In contrast, ligand-free structures are more accessible but suffer from reduced docking performance due to pocket geometries being less suited for ligand accommodation in apo structures. Traditional methods for artificially inducing these conformations, such as molecular dynamics simulations, are computationally expensive. In this work, we introduce Sesame, a generative model designed to predict this conformational change efficiently. By generating geometries better suited for ligand accommodation at a fraction of the computational cost, Sesame aims to provide a scalable solution for improving virtual screening workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05302 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sesame: Opening the door to protein pockets Miñán, Raúl Perez-Lopez, Carles Iglesias, Javier Ciudad, Álvaro Molina, Alexis Biomolecules Artificial Intelligence Molecular docking is a cornerstone of drug discovery, relying on high-resolution ligand-bound structures to achieve accurate predictions. However, obtaining these structures is often costly and time-intensive, limiting their availability. In contrast, ligand-free structures are more accessible but suffer from reduced docking performance due to pocket geometries being less suited for ligand accommodation in apo structures. Traditional methods for artificially inducing these conformations, such as molecular dynamics simulations, are computationally expensive. In this work, we introduce Sesame, a generative model designed to predict this conformational change efficiently. By generating geometries better suited for ligand accommodation at a fraction of the computational cost, Sesame aims to provide a scalable solution for improving virtual screening workflows. |
| title | Sesame: Opening the door to protein pockets |
| topic | Biomolecules Artificial Intelligence |
| url | https://arxiv.org/abs/2509.05302 |