PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks
<|reference_start|>PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks: 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.<|reference_end|>
@article{schapin2024playmolecule,
    title={PlayMolecule pKAce: Small Molecule Protonation through Equivariant
  Neural Networks},
    author={Nikolai Schapin, Maciej Majewski, Mariona Torrens-Fontanals, Gianni De
  Fabritiis},
    journal={arXiv preprint arXiv:2407.11103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11103},
    primaryClass={q-bio.QM cs.LG}
}