Skin sensitizer classification using dual-input machine learning model

Skin sensitization is an important aspect of occupational and consumer safety. Because of the ban on animal testing for skin sensitization in Europe, in silico approaches to predict skin sensitizers are needed. Recently, several machine learning approaches, such as the gradient boosting decision tree (GBDT) and deep neural networks (DNNs), have been applied to chemical reactivity prediction, showing remarkable accuracy. Herein, we performed a study on DNN- and GBDT-based modeling to investigate their potential for use in predicting skin sensitizers. We separately input two types of chemical properties (physical and structural properties) in the form of one-hot labeled vectors into single- and dual-input models. All the trained dual-input models achieved higher accuracy than single-input models, suggesting that a multi-input machine learning model with different types of chemical properties has excellent potential for skin sensitizer classification.