# PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks

arXiv: https://arxiv.org/abs/2407.11103 · HTML: https://mlsanity.com/paper/2407.11103/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields](https://mlsanity.com/paper/2206.07697/) (2022)
- [TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials](https://mlsanity.com/paper/2202.02541/) (2022)
- [TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials](https://mlsanity.com/paper/2306.06482/) (2023)
- [Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces](https://mlsanity.com/paper/2111.13964/) (2021)
- [Geometric Graph Learning with Extended Atom-Types Features for Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2301.06194/) (2023)
- [Machine Learning Small Molecule Properties in Drug Discovery](https://mlsanity.com/paper/2308.12354/) (2023)

## 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.