Epik: pKa and Protonation State Prediction through Machine Learning

Epik version 7 is a software program that uses machine learning for predicting the pKa values and protonation state distribution of complex, druglike molecules. Using an ensemble of atomic graph convolutional neural networks (GCNNs) trained on over 42,000 pKa values across broad chemical space from both experimental and computed origins, the model predicts pKa values with 0.42 and 0.72 pKa 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.

Automated summary

Epik version 7 is a software program that uses machine learning to predict the pKa values and protonation state distribution of complex, druglike molecules. It achieves high accuracy, with median absolute and root mean square errors of 0.42 and 0.72 pKa units, respectively, across seven test sets. Epik version 7 also provides protonation states and recovers 95% of the most populated states compared to previous versions. It is fast, taking only an average of 47 ms per ligand, making it suitable for evaluating protonation states and generating compound libraries for exploration of chemical space.