Machine Learning for Solubility Prediction

Abstract

Abstract The solubility of a chemical in water is a critical parameter in drug development and other fields such as environmental chemistry and agrochemistry, but its in silico prediction presents a formidable challenge. Here, we apply a suite of graph-based machine learning algorithms to the benchmark problems posed over several years in international ``solubility challenges'', and also to our own newly-compiled dataset of over 11,000 compounds. We find that graph convolutional networks (GCNs) and graph attention networks (GATs) both show excellent predictive power against these datasets. Although not executed under competition conditions, these approaches achieve better scores in several instances than the best models available at the time. They offer an incremental, but still significant, improvement when compared against a range of existing cheminformatics approaches.