PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| author | Schapin, Nikolai Majewski, Maciej Torrens-Fontanals, Mariona De Fabritiis, Gianni |
| author_facet | Schapin, Nikolai Majewski, Maciej Torrens-Fontanals, Mariona De Fabritiis, Gianni |
| contents | Small molecule protonation is an important part of the preparation of small molecules for many types of computational chemistry protocols. For this, a correct estimation of the pKa values of the protonation sites of molecules is required. In this work, we present pKAce, a new web application for the prediction of micro-pKa values of the molecules' protonation sites. We adapt the state-of-the-art, equivariant, TensorNet model originally developed for quantum mechanics energy and force predictions to the prediction of micro-pKa values. We show that an adapted version of this model can achieve state-of-the-art performance comparable with established models while trained on just a fraction of their training data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11103 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks Schapin, Nikolai Majewski, Maciej Torrens-Fontanals, Mariona De Fabritiis, Gianni Quantitative Methods Machine Learning 92-08 J.3 Small molecule protonation is an important part of the preparation of small molecules for many types of computational chemistry protocols. For this, a correct estimation of the pKa values of the protonation sites of molecules is required. In this work, we present pKAce, a new web application for the prediction of micro-pKa values of the molecules' protonation sites. We adapt the state-of-the-art, equivariant, TensorNet model originally developed for quantum mechanics energy and force predictions to the prediction of micro-pKa values. We show that an adapted version of this model can achieve state-of-the-art performance comparable with established models while trained on just a fraction of their training data. |
| title | PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks |
| topic | Quantitative Methods Machine Learning 92-08 J.3 |
| url | https://arxiv.org/abs/2407.11103 |