In Silico Design and Validation of a Novel HPPD‐Inhibiting Herbicide Candidate Based on Benzofuran and Arylthioacetic Acid Scaffolds

TL;DR

Researchers designed a new herbicide candidate using computational methods that could be more effective than existing ones.

Contribution

A novel HPPD-inhibiting herbicide candidate (P1) was identified using integrated computational approaches.

Findings

Candidate P1 showed lower binding energy and stable interactions compared to mesotrione.

MIA-QSAR, docking, and MCPB.py simulations were successfully combined to design the inhibitor.

P1 is a benzofuran-triketone hybrid with strong predicted herbicidal activity.

Abstract

Inhibition of 4‐hydroxyphenylpyruvate dioxygenase (HPPD) is a well‐established strategy for weed control, yet the emergence of resistance underscores the need for more potent inhibitors. In this study, datasets of benzofuran analogues and arylthioacetic acid–derived triketones were analyzed using multivariate image analysis of quantitative structure–activity relationships (MIA‐QSARs) to guide the identification of promising candidates. Molecular docking was conducted on HPPD, including Co(II) substitution to explore metal–ligand interactions, and molecular dynamics simulations, parametrized with MCPB.py, evaluated the stability and interaction patterns of the complexes. Fourteen candidates were proposed, six of which exhibited higher predicted activity and improved performance relative to mesotrione. Among them, candidate P1 emerged as the most promising, reproducing key interactions of mesotrione while displaying lower binding energy and stable convergence during dynamics. These results demonstrate P1 as a novel HPPD inhibitor and highlight the utility of combining MIA‐QSAR, docking, and MCPB.py–parametrized dynamics in rational metalloenzyme inhibitor design.

A benzofuran‐triketone hybrid emerged as the most promising 4‐hydroxyphenylpyruvate inhibitor, showing the highest predicted activity and strongest binding compared to the best experimental compound. Free computational tools guided candidate selection, predicted interactions with the enzyme, and confirmed stable binding. These findings highlight the compound's potential as a novel herbicide, comparable to the commercial standard mesotrione, and demonstrate the value of integrated computational approaches for designing effective enzyme inhibitors.

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