Development of a GCN-based model to predict in vitro phototoxicity from the chemical structure and HOMO-LUMO gap

The interaction between sunlight and drugs can lead to phototoxicity in patients who have received such drugs. Phototoxicity assessment is a regulatory requirement globally and one of the main toxicity screening steps in the early stages of drug discovery. An in silico-in vitro approach has been utilized mainly for toxicology assessments at these stages. Although several quantitative structure-activity relationship (QSAR) models for phototoxicity have been developed, in silico technology to evaluate phototoxicity has not been well established. In this study, we attempted to develop an artificial intelligence (AI) model to predict the in vitro Neutral Red Uptake Phototoxicity Test results from a chemical structure and its derived information. To accomplish this, we utilized an open-source software library, kMoL. kMoL employs a graph convolutional neural networks (GCN) approach, which allows it to learn the data for the specified chemical structure. kMoL also utilizes the integrated gradient (IG) method, enabling it to visually display the substructures contributing to any positive results. To construct this AI model, we used only the chemical structure as a basis, then added the descriptors and the HOMO-LUMO gap, which was obtained from quantum chemical calculations. As a result, the assortment of chemical structures and the HOMO-LUMO gap produced an AI model with high discrimination performance, and an F1 score of 0.857. Additionally, our AI model could visualize the substructures involved in phototoxicity using the IG method. Our AI model can be applied as a toxicity screening method and could enhance productivity in drug development.