Overfitting in machine learning models for predicting partial atomic charges of drug-like molecules

Overfitting in machine learning models for partial atomic charges was investigated for highly heterogeneous datasets common in medicinal chemistry. Random forest and multilayer perceptron models were trained and validated on a specially clustered dataset of drug-like molecules. Analysis of standard quality metrics for reproducing RESP charges showed that the trained models exhibit no evidence of overfitting.