Epik: p <i>K</i> _a and Protonation State Prediction through Machine Learning

Abstract

Epik version 7 is a software program that uses machine learning for predicting the p<i>K</i>_a values and protonation state distribution of complex, druglike molecules. Using an ensemble of atomic graph convolutional neural networks (GCNNs) trained on over 42,000 p<i>K</i>_a values across broad chemical space from both experimental and computed origins, the model predicts p<i>K</i>_a values with 0.42 and 0.72 p<i>K</i>_a unit median absolute and root mean square errors, respectively, across seven test sets. Epik version 7 also generates protonation states and recovers 95% of the most populated protonation states compared to previous versions. Requiring on average only 47 ms per ligand, Epik version 7 is rapid and accurate enough to evaluate protonation states for crucial molecules and prepare ultra-large libraries of compounds to explore vast regions of chemical space. The simplicity and time required for the training allow for the generation of highly accurate models customized to a program's specific chemistry.