Using explainable machine learning to predict the irritation and corrosivity of chemicals on eyes and skin

Contact with specific chemicals often results in corrosive and irritative responses in the eyes and skin, playing a pivotal role in assessing the potential hazards of personal care products, cosmetics, and industrial chemicals to human health. While traditional animal testing can provide valuable information, its high costs, ethical controversies, and significant demand for animals limit its extensive use, particularly during preliminary screening stages. To address these issues, we adopted a computational modeling approach, integrating 3316 experimental data points on eye irritation and 3080 data points on skin irritation, to develop various machine learning and deep learning models. Under the evaluation of the external validation set, the best-performing models for the two tasks achieved balanced accuracies (BAC) of 73.0 % and 75.1 %, respectively. Furthermore, interpretability analyses were conducted at the dataset level, molecular level, and atomic level to provide insights into the prediction outcomes. Analysis of substructure frequencies identified structural alert fragments within the datasets. This information serves as a reference for identifying potentially irritating chemicals. Additionally, a user-friendly visualization interface was developed, enabling non-specialists to easily predict eye and skin irritation potential. In summary, our study provides a new avenue for the assessment of irritancy potential in chemicals used in pesticides, cosmetics, and ophthalmic drugs.