Training Set for the Prediction of Partial Charges

The vastness of the chemical space makes approaches using building blocks challenging. The most common approach is therefore an invidual parametriza- tion of each compound by deriving partial charges from semi-empirical or ab initio calculations and inheriting the bonded and van der Waals (Lennard-Jones) parameters from a biomolecular force field.

The quality of the partial charges generated in this fashion depends on the level of the quantum-chemical calculation as well as on the extraction procedure used. Here, we present a machine learning (ML) based approach for predicting partial charges extracted from density functional theory (DFT) electron densities. The training set was chosen with the goal to provide a broad coverage of the known chemical space of drug-like molecules.