Artificial intelligence-based molecular property prediction of photosensitising effects of drugs

Drug-induced photosensitivity is a potential adverse event of many drugs and chemicals used across a wide range of specialties in clinical medicine. In the present study, we investigated the feasibility of predicting the photosensitising effects of drugs and chemical compounds via state-of-the-art artificial intelligence-based workflows. A dataset of 2200 drugs was used to train three distinct models (logistic regression, XGBoost and a deep learning model (Chemprop)) to predict photosensitising attributes. Labels were obtained from a list of previously published photosensitisers by string matching and manual validation. External evaluation of the different models was performed using the tox21 dataset. ROC-AUC ranged between 0.8939 (Chemprop) and 0.9525 (XGBoost) during training, while in the test partition it ranged between 0.7785 (Chemprop) and 0.7927 (XGBoost). Analysis of the top 200 compounds of each model resulted in 55 overlapping molecules in the external validation set. Prediction scores in fluoroquinolones within this subset corresponded well with culprit substructures such as fluorinated aryl halides suspected of mediating photosensitising effects. All three models appeared capable of predicting photosensitising effects of chemical compounds. However, compared to the simpler model, the complex models appeared to be more confident in their predictions as exhibited by their distribution of prediction scores.