In Silico Design Vectoring for Safer Pesticides

Systems-based design of safe yet high-performing chemicals is a formidable challenge and a yet-to-be-realized goal in the upstream of new product development, especially in the non-specialty chemical sectors. Here, we posit that a solution can be found at the intersection of (green) chemistry and toxicology, leveraging computational modeling to bridge these disciplines and provide the necessary multifaceted analysis at a manageable cost. We outline an in silico framework that mimics computational drug discovery to optimize properties of agrochemicals and pesticides’ (biologically-) active ingredients in particular. Specifically, the nexus of computational chemistry techniques and modern statistics is discussed in the context of systems-based design vectors, which can aid in redesign of existing agrochemicals to improve their characteristics or in ab initio design of novel pesticides from biobased (i.e., renewable) feedstock. The latter is particularly appealing, as persistence and unintended toxicity concerns can be more easily managed, and selective toxicity (i.e., performance) is enhanced in a controlled optimization process of the underlying trade-offs.