Improving Small Molecule pK( a ) Prediction Using Transfer Learning With Graph Neural Networks

Enumerating protonation states and calculating microstate pK( a ) values of small molecules is an important yet challenging task for lead optimization and molecular modeling. Commercial and non-commercial solutions have notable limitations such as restrictive and expensive licenses, high CPU/GPU hour requirements, or the need for expert knowledge to set up and use. We present a graph neural network model that is trained on 714,906 calculated microstate pK( a ) predictions from molecules obtained from the ChEMBL database. The model is fine-tuned on a set of 5,994 experimental pK( a ) values significantly improving its performance on two challenging test sets. Combining the graph neural network model with Dimorphite-DL, an open-source program for enumerating ionization states, we have developed the open-source Python package pkasolver, which is able to generate and enumerate protonation states and calculate pK( a ) values with high accuracy.