Multivariate image analysis applied to quantitative structure-activity relationships and docking studies of recent hydroxyphenylpyruvate deoxygenase inhibitors.

ABSTRACT

BACKGROUND: Mesotrione is a triketone widely used as an inhibitor of the hydroxyphenylpyruvate deoxygenase (HPPD) enzyme. However, new agrochemicals should be developed continuously to tackle the problem of herbicide resistance. Two sets of mesotrione analogs have been synthesized recently and they have demonstrated successful phytotoxicity against weeds. In this study, these compounds were joined to form a single data set and the HPPD inhibition of this enlarged library of triketones was modeled using multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR). Docking studies were also carried out to validate the MIA-QSAR findings and to aid the interpretation of ligand-enzyme interactions responsible for the bioactivity (pIC50 ). RESULTS: The MIA-QSAR models based on van der Waals radii (rvdW ), electronegativity (ε), and the rvdW /ε ratio as molecular descriptors were both predictive to an acceptable degree (r2 ≥ 0.80, q2 ≥ 0.68 and r2 pred ≥ 0.68). Subsequently, partial least squares (PLS) regression parameters were applied to predict the pIC50 values of newly proposed derivatives, yielding a few promising agrochemical candidates. The calculated log P for most of these derivatives was found to be higher than that of mesotrione and the library compounds, indicating that they should be less prone to leach out and contaminate groundwater. CONCLUSION: Multivariate image analysis descriptors corroborated by docking studies were capable of modeling the herbicidal activities of 68 triketones reliably. Due to the substituent effects at the triketone framework, particularly of a nitro group in R3 , promising analogs could be designed. The P9 proposal demonstrated higher calculated activity and log P than commercial mesotrione. © 2023 Society of Chemical Industry.