TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity

Herein, a robust and reproducible eXplainable Artificial
Intelligence
(XAI) approach is presented, which allows prediction of developmental
toxicity, a challenging human-health endpoint in toxicology. The application
of XAI as an alternative method is of the utmost importance with developmental
toxicity being one of the most animal-intensive areas of regulatory
toxicology. In this work, the established CAESAR (Computer Assisted
Evaluation of industrial chemical Substances According to Regulations)
training set made of 234 chemicals for model learning is employed.
Two test sets, including as a whole 585 chemicals, were instead used
for validation and generalization purposes. The proposed framework
favorably compares with the state-of-the-art approaches in terms of
accuracy, sensitivity, and specificity, thus resulting in a reliable
support system for developmental toxicity ensuring informativeness,
uncertainty estimation, generalization, and transparency. Based on
the eXtreme Gradient Boosting (XGB) algorithm, our predictive model
provides easy interpretative keys based on specific molecular descriptors
and structural alerts enabling one to distinguish toxic and nontoxic
chemicals. Inspired by the Organisation for Economic Co-operation
and Development (OECD) principles for the validation of Quantitative
Structure–Activity Relationships (QSARs) for regulatory purposes,
the results are summarized in a standard report in portable document
format, enclosing also details concerned with a density-based model
applicability domain and SHAP (SHapley Additive exPlanations) explainability,
the latter particularly useful to better understand the effective
roles played by molecular features. Notably, our model has been implemented
in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for
Scientific and Industry Applications), a free of charge web platform
available at http://tiresia.uniba.it.