Phototoxicity, sometimes in the literature referred to as photo-irritation, is a chemically induced reaction requiring light. While it is generally accepted that phototoxicity testing can be performed in the majority of cases in vitro (i.e. without the use of experimental animals), these tests may sometimes provide contradictory predictions. Understanding the mechanisms of initiating events based on the molecule's structure and its ability to reach the excited state and consequently generate ROS enables the creation of predictive QSAR for this adverse outcome. The ability to predict the phototoxicity potential via a QSAR model is beneficial in reducing the number of mechanical in vitro/in chemico tests needed to demonstrate absence of phototoxicity and it is very helpful in the overall safety assessment process. The QSAR prediction model presented here focused on developing a robust platform freely available on the web via the link http://mltox.online to provide interpretable predictions of the phototoxicity of tested molecules. Great attention was devoted to interpretability and explainability of the prediction results. The web application allows the user to input a chemical by CAS number, SMILES code or trivial name. The user can choose between simple prediction or advanced tools options. These extended tools include the artificial intelligence explainability of model prediction using XSMILES (interactive visualization technique to support the interpretation of SMILES) and SHAP values (impact each element on the prediction). The comprehensive tools in question allow the user to explore the properties of phototoxic substances and to understand the prediction outcomes better.
Keywords: MLtox; Phototoxicity prediction; QSAR.
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