Lewer Jessica M, Stickelman Zachary R, Huang Jessica H, Peloquin John F, Kostal Jakub
Department of Chemistry, The George Washington University, 800 22nd St NW, Ste 4000, Washington, DC 20052-0066, USA.
Sci Adv. 2022 Apr;8(13):eabn2058. doi: 10.1126/sciadv.abn2058. Epub 2022 Mar 30.
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.
设计具有可调节降解特性和最低生态毒性的农药是绿色化学面临的重大挑战之一。虽然计算方法在预测毒理学方面已受到关注,但目前的方法缺乏基于系统的化学开发所需的必要多方面方法和设计导向工具。在此,我们报告了一个分层计算框架,该框架基于机械推导的物理化学性质和电子参数的临界值,将间接光降解的动力学和热力学与生态毒性及性能预测相结合。通过对实验数据进行广泛验证,并应用于美国环境保护局登记的700种农药,我们这种简单却强大的方法可用于筛选现有分子,以识别具有理想特性、可投入应用的候选物。通过将结构属性与基于过程的结果相联系,并量化安全性、消耗和性能方面的权衡,我们的方法为新型农药的合理设计提供了一个用户友好的路线图。