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arXiv · 2407.11103

PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks

Nikolai Schapin·Maciej Majewski·Mariona Torrens-Fontanals·Gianni De Fabritiis
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Abstract

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.

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Nikolai Schapin, Maciej Majewski, Mariona Torrens-Fontanals, Gianni De Fabritiis. 2024-07-15. PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks. https://arxiv.org/abs/2407.11103

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