Automated Grouping of Nanomaterials and Read-Across Prediction of Their Adverse Effects Based on Mathematical Optimization.

In this study, a computational workflow is presented for grouping engineered nanomaterials (ENMs) and for predicting their toxicity-related end points. A mixed integer-linear optimization program (MILP) problem is formulated, which automatically filters out the noisy variables, defines the grouping boundaries, and develops specific to each group predictive models. The method is extended to the multidimensional space, by considering the ENM characterization categories (e.g., biological, physicochemical, biokinetics, image etc.) as different dimensions. The performance of the proposed method is illustrated through the application to benchmark data sets and comparison with alternative predictive modeling approaches. The trained models using the above data sets were made publicly available through a user-friendly web service.