4.7 Article

HerbiPAD: a free web platform to comprehensively analyze constitutive property and herbicide-likeness to estimate chemical bioavailability

Journal

PEST MANAGEMENT SCIENCE
Volume 77, Issue 3, Pages 1273-1281

Publisher

JOHN WILEY & SONS LTD
DOI: 10.1002/ps.6140

Keywords

herbicide‐ likeness; physicochemical properties; bioavailability; database

Funding

  1. National Key RD Program [2018YFD0200100]
  2. National Natural Science Foundation of China [21772059, 91853127, 31960548]
  3. Program of Introducing Talents of Discipline to Universities of China (111 Program) [D20023]

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HerbiPAD is a free web platform designed to evaluate herbicide-likeness through integrated qualitative and quantitative analyses, with high accuracy in distinguishing known herbicides from nonherbicides. It provides a user-friendly interface for noncomputational experts to quickly understand the herbicide-likeness of compounds.
BACKGROUND Herbicides, as efficient weed control measures, play a crucial role in ensuring food security. The emergence of herbicide-resistant weeds has negatively affected food security and promoted the demand for new and improved herbicides. The balance between bioavailability and the potency of a compound is one of the most pressing challenges in the development of novel ideal herbicides. Herbicide-likeness analysis is crucial for the evaluation of this balance and thus may help to address this issue. Many herbicide-likeness analysis methods have been developed to screen potential novel lead compounds. However, there remains a lack of user-friendly and integrated tools to comprehensively evaluate herbicide-likeness. RESULTS Herbicide-likeness of compounds was assessed through integrated analysis incorporating the physicochemical properties of commercial herbicides, a qualitative rule, and three quantitative scoring functions developed for evaluating herbicide-likeness. HerbiPAD () is a free web platform integrated with the collected database and scoring model. This platform contains 542 approved herbicides and > 29 000 physicochemical descriptors. The accuracy of HerbiPAD in distinguishing known herbicides from nonherbicides was 84.2%. In the case study, HerbiPAD evaluated 60 new compounds from seven different herbicide targets, and the accuracy of predicting better bioavailability was 83.3%. CONCLUSIONS HerbiPAD was designed to quickly and efficiently evaluate herbicide-likeness by integrating qualitative and quantitative analyses. The simple and effective interpretation of the analysis interface may help noncomputational experts understand herbicide-likeness.

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