Assessing interaction recovery of predicted protein-ligand poses
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916415745818624 |
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| author | Errington, David Schneider, Constantin Bouysset, Cédric Dreyer, Frédéric A. |
| author_facet | Errington, David Schneider, Constantin Bouysset, Cédric Dreyer, Frédéric A. |
| contents | The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking methods or even to predict all-atom protein-ligand complex structures. Most contemporary studies focus on the accuracy and physical plausibility of ligand placement to determine pose quality, often neglecting a direct assessment of the interactions observed with the protein. In this work, we demonstrate that ignoring protein-ligand interaction fingerprints can lead to overestimation of model performance, most notably in recent protein-ligand cofolding models which often fail to recapitulate key interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20227 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Assessing interaction recovery of predicted protein-ligand poses Errington, David Schneider, Constantin Bouysset, Cédric Dreyer, Frédéric A. Biomolecules Machine Learning The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking methods or even to predict all-atom protein-ligand complex structures. Most contemporary studies focus on the accuracy and physical plausibility of ligand placement to determine pose quality, often neglecting a direct assessment of the interactions observed with the protein. In this work, we demonstrate that ignoring protein-ligand interaction fingerprints can lead to overestimation of model performance, most notably in recent protein-ligand cofolding models which often fail to recapitulate key interactions. |
| title | Assessing interaction recovery of predicted protein-ligand poses |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2409.20227 |