Artificial Intelligence for the Discovery of Safe and Effective Flame Retardants

Abstract

Organophosphorus flame retardants (OPFRs) are important chemical additives that are used in commercial products. However, owing to increasing health concerns, the discovery of new OPFRs has become imperative. Herein, we propose an explainable artificial intelligence-assisted product design (AIPD) methodological framework for screening novel, safe, and effective OPFRs. Using a deep neural network, we established a flame retardancy prediction model with an accuracy of 0.90. Employing the SHapley Additive exPlanations approach, we have identified the Morgan 507 (P═N connected to a benzene ring) and 114 (quaternary carbon) substructures as promoting units in flame retardancy. Subsequently, approximately 600 compounds were selected as OPFR candidates from the ZINC database. Further refinement was achieved through a comprehensive scoring system that incorporated absorption, toxicity, and persistence, thereby yielding six prospective candidates. We experimentally validated these candidates and identified compound Z2 as a promising candidate, which was not toxic to zebrafish embryos. Our methodological framework leverages AIPD to effectively guide the discovery of novel flame retardants, significantly reducing both developmental time and costs.