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Medicinal Chemistry

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ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

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Mini-Review Article

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment

Author(s): Yunfeng Yang, Junjie Zhong, Songyu Shen, Jiajun Huang, Yihan Hong, Xiaosheng Qu*, Qin Chen* and Bing Niu*

Volume 20, Issue 1, 2024

Published on: 10 May, 2023

Page: [2 - 16] Pages: 15

DOI: 10.2174/1573406419666230406091759

Price: $65

Abstract

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

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