Development of high-performing pesticides with tunable degradation properties is vital to increasing the safety and effectiveness of tomorrow’s analogs. Chromophoric dissolved organic matter in the excited triple state (3CDOM*) is known to play a key role in the removal of pesticides via indirect photodegradation. However, the potential of these transformations to guide the design of safer chemicals has not yet been fully realized. Here, we report a two-tier computational framework developed to probe and predict both kinetics and thermodynamics of 3CDOM*-pesticide interactions. In the first tier, robust in silico models were constructed by fitting free energies obtained from density functional theory (DFT) calculations to cell potentials and second-order rate constants for the 3CDOM*-pesticide electron transfer. In the second tier, Gibbs free energies and corresponding free energy barriers, determined in solution using the Marcus theory, were applied to develop a quick yet accurate screening approach based on the frontier molecular orbital (FMO) Theory. Being highly mechanistic and spanning ca. 1500 unique 3CDOM*-pesticide interactions, our approach is both robust and broadly applicable. To that end, the outcomes of our computational models were integrated into an easy-to-use decision framework that can guide structure-based design of less persistent pesticide analogs.

Rational design of pesticides with tunable degradation properties and minimal ecotoxicity is among the grand challenges of green chemistry. While computational approaches have gained traction in predictive toxicology, current methods lack the necessary multifaceted approach and design-vectoring tools needed for system-based chemical development. Here, we report a tiered computational framework, which integrates kinetics and thermodynamics of indirect photodegradation with predictions of ecotoxicity and performance, based on cutoff values in mechanistically derived physicochemical properties and electronic parameters. Extensively validated against experimental data and applied to 700 pesticides on the U.S. Environmental Protection Agency’s registry, our simple yet powerful approach can be used to screen existing molecules to identify application-ready candidates with desirable characteristics. By linking structural attributes to process-based outcomes and by quantifying trade-offs in safety, depletion, and performance, our method offers a user-friendly roadmap to rational design of novel pesticides.

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