PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks
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Abstract (arXiv)
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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References · 6
- 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 467
- 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
- 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentialscited by 53
- 2021Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spacescited by 5
- 2023Geometric Graph Learning with Extended Atom-Types Features for Protein-Ligand Binding Affinity Predictioncited by 5
- 2023Machine Learning Small Molecule Properties in Drug Discoverycited by 5
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