  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.
