Abstract

Rational design of chemicals with tunable degradation properties, high efficacy and minimal toxicity is among the grand challenges of green chemistry. While computational modeling has gained traction in predictive toxicology, current methods lack the necessary multifaceted, principle-driven approach and design-vectoring tools to enable systems-based chemical development. To that end, our group has proposed a tiered computational framework, which integrates modern statistical methods with quantum mechanics modeling and molecular simulations to optimize chemical properties and help quantify the underlying trade-offs. In this talk, we will showcase the architecture of our approach, and its application to the re-design and de novo design of safer pesticides. In parallel, we will discuss the development of predictive models in support of hazard and alternatives assessment for a host of endpoints important to the pharmaceutical and personal care industries.