1. Introduction

Graph-pKₐ is a quantitative structure-property relationship (QSPR) model to predict acid dissociation constant (pKₐ) of small molecules in aqueous solution. Combining multi-instance learning into graph neural network, Graph-pKₐ not only performs well in predicting macro pKₐ, but more significantly, can learn the micro pKₐ values of atoms through training against the macro pKₐ values of molecules.
Here, we provided a Web version of Graph-pKₐ model that was trained with about 17000 pKₐ datapoints.

2. Reference

Jiacheng Xiong, Zhaojun Li, Guangchao Wang, et al., Multi-instance learning of graph neural networks for aqueous pKa prediction, Bioinformatics, Volume 38, Issue 3, 1 February 2022, Pages 792–798, doi: 10.1093/bioinformatics/btab714