Agrochemicals have been critical to the production of food and fiber, as well as the control of vectors of disease. The need for the discovery and development of new agrochemicals continues unabated due to the loss of existing products through the development of resistance, the desire for products with more favorable environmental and toxicological profiles, shifting pest spectra, and changing agricultural needs and practices. As presented in the associated analysis of the agrochemical industry, the rising costs and complexities of agrochemical discovery have, in part, led to increasing consolidation, especially in the USA and Europe. However, as demonstrated by the present analysis, the discovery of new agrochemicals continues in spite of the challenges. © 2016 Society of Chemical Industry.

The 1 H and 13 C NMR chemical shifts of 48 industrially preferred solvents in six commonly used deuterated NMR solvents (CDCl 3 , acetone-d 6 , DMSO-d 6 , acetonitrile-d 3 , methanol-d 4 , and D 2 O) are reported. This work supplements the compilation of NMR data published by Gottlieb, Kotlyar, and Nudelman (J. Org. Chem. 1997, 62, 7512) by providing spectral parameters for solvents that were not commonly utilized at the time of their original report. Data are specifically included for solvents, such as 2-Me-THF, n-heptane, and iso-propyl acetate, which are being used more frequently as the chemical industry aims to adopt greener, safer, and more sustainable solvents. These spectral tables simplify the identification of these solvents as impurities in NMR spectra following their use in synthesis and workup protocols.

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The relentless need for the discovery and development of new agrochemicals continues as a result of driving forces such as loss of existing products through the development of resistance, the necessity for products with more favorable environmental and toxicological profiles, shifting pest spectra, and the changing agricultural needs and practices of the farming community. These new challenges underscore the demand for novel, high-quality starting points to accelerate the discovery of new agrochemicals that address market challenges. This article discusses the efforts to identify the optimum ranges of physicochemical properties of agrochemicals through analysis of modern commercial products. Specifically, we reviewed literature studies examining physicochemical property effects and analyzed the properties typical of successful fungicides, herbicides, and insecticides (chewing and sap-feeding pests). From the analysis, a new set of physicochemical property guidelines for each discipline, as well as building block class, are proposed. These new guidelines should significantly aid in the discovery of next-generation agrochemicals. © 2018 Society of Chemical Industry.

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With the anticipated population growth in the coming decades, the changing regulatory environment, and the continued emergence of resistance to commercial pesticides, there is a constant need to discover new lead chemistries with novel modes of action. We have established a portfolio of approaches to accelerate lead generation. One of these approaches capitalizes on the rich bioactivity of natural products (NPs), highlighted by the numerous examples of NP‐based crop protection compounds. Within Corteva Agriscience and the affiliated preceding companies, NPs have been a fruitful approach, for nearly three decades, to identifying and bringing to the market crop protection products inspired by or originating from NPs, . Included in these NP‐based crop protection products are the spinosyns family of insecticides, and those from more recent areas of NP‐based fungicidal chemistry, as highlighted in this perspective. © 2019 Society of Chemical Industry

In this paper we show how graphical analysis of ligand effect data for families of ligands should be combined with regression analysis in order to gain a more self-consistent interpretation of regression results. If a steric threshold shows up in the graphical analysis of the data for the trialkyl ligands (AR 3 ), then the threshold must show up in regression analysis of the full set of data. Similarly, the dependence of the data for the isosteric ligands A(p-XC 6 H 4 ) 3 must have the same dependence on the electronic parameter χ that we find for the regression analysis for the full set of data. Finally, graphical analysis for both AR 3 and A(p-XC 6 H 4 ) 3 families shows vividly whether steric effects and/or aryl effects belong in the full analysis. Thus, for results of any regression analysis to be acceptable, they must be consistent with the results of graphical analysis.

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