SbD4Skin by EosCloud: Integrating multi-view molecular representation for predicting skin sensitization, irritation,...

Assessing chemical toxicity is essential for understanding potential risks to human health. However, ethical, financial, and scientific challenges have driven the demand for non-animal testing methods. This study introduces a computational framework that leverages diverse molecular representations, including MACCS keys, Morgan fingerprints, and Mordred descriptors, to predict skin sensitization, irritation/corrosion, and acute dermal toxicity. Different molecular representations for skin toxicity-related endpoints were first evaluated using three machine learning algorithms (Random Forest, Support Vector Machine, and k-Nearest Neighbors), then combined into a unified input space for training a fully connected neural network (FCNN). Comparative analyses indicate that this multi-view FCNN model offers superior or comparable predictive performance relative to single-representation models, achieving area under the curve (AUC) values of up to 0.91 for irritation/corrosion, 0.88 for sensitization, and 0.82 for acute dermal toxicity on test sets. Additional validation on known toxicants further confirms the framework's robustness, correctly identifying 0.86 of skin sensitizers, 0.89 of irritants, and 0.86 of dermally toxic compounds. Shapley Additive exPlanation (SHAP) analyses highlight the most influential molecular features, providing mechanistic insights and enhancing model transparency. To promote broader adoption and reduce reliance on animal testing, the developed models are freely available through the SbD4Skin (Safe by Design for Skin) web platform (https://eoscloud.entelos.eu/ssbd4chem/sbd4skin/), offering a user-friendly tool for chemical risk assessment and regulatory decision-making. The dataset and model developed in this study have been FAIRified and made available in machine-actionable and modelling-ready formats, supporting transparency, reuse, and regulatory acceptance.