Accelerating Cosmetic Innovation Through Next-Generation Computational Toxicology: From Structural Alerts to Molecular Dynamics and Artificial Intelligence for Skin Sensitization Assessment

Skin sensitization remains a critical toxicological endpoint in cosmetic ingredient development, particularly under accelerated innovation cycles and increasingly stringent safety requirements. Although NAMs have achieved substantial regulatory maturity for this endpoint, most computational approaches—including structural alerts, read-across, and quantitative structure–activity relationship models—rely predominantly on chemical structure and statistical associations, with limited representation of the molecular processes underlying sensitization. This perspective proposes a transparent and testable computational framework that integrates conventional chemical descriptors, reaction-domain information, covalent docking, molecular dynamics simulations, and machine learning methods. The framework is organized around the skin sensitization adverse outcome pathway and focuses on generating mechanistically informed descriptors associated with the molecular initiating event and selected molecular processes related to keratinocyte activation. These descriptors include reactive geometry, residue accessibility, interaction persistence, conformational behavior, and perturbation hypotheses involving the KEAP1–NRF2 regulatory axis. Rather than replacing established experimental NAMs or DAs, the proposed workflow is intended as a complementary, tiered evidence layer for the early prioritization of structurally characterized cosmetic ingredients and for guiding subsequent experimental testing. Its future value will depend on module-level validation, demonstration of incremental predictive performance, explicit applicability-domain and uncertainty assessment, computational scalability, and prospective comparison with established NAM outcomes.