Nakayama Yuki, Morishita Saki, Doi Hayato, Hirano Tatsuya, Kaneko Hiromasa
Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.
Hokko Chemical Industry Co., Ltd., 2165, Toda, Atsugi-shi, Kanagawa 243-0023, Japan.
ACS Omega. 2024 Apr 9;9(16):18488-18494. doi: 10.1021/acsomega.4c00655. eCollection 2024 Apr 23.
Pesticides are widely used to improve crop productivity by eliminating weeds and pests. Conventional pesticide development involves synthesizing compounds, testing their activities, and studying their effects on the ecosystem. However, as pesticide discovery has an extremely low success rate, many compounds must be synthesized and tested. To overcome the high human, financial, and time costs of this process, machine learning is attracting increasing attention. In this study, we used machine learning for the molecular design of novel seed compounds for herbicides and insecticides. Classification models were constructed by using compounds that had been tested as herbicides and insecticides, and an inverse analysis of the constructed models was conducted. In the molecular design of herbicides, we proposed 186 new samples as herbicides using ensemble learning and a method for expressing explanatory variables that consider the relationships among eight weed species. For the molecular design of insecticides, we used undersampling and ensemble learning for the analysis of unbalanced data. Based on approximately 340,000 compounds, 12 potential insecticides were proposed, of which 2 exhibited actual activity when tested. These results demonstrate the potential of the developed machine-learning method for rapidly identifying novel herbicides and insecticides.
农药被广泛用于通过消除杂草和害虫来提高作物产量。传统的农药开发包括合成化合物、测试其活性以及研究其对生态系统的影响。然而，由于农药发现的成功率极低，必须合成和测试许多化合物。为了克服这一过程中高昂的人力、财力和时间成本，机器学习正受到越来越多的关注。在本研究中，我们将机器学习用于除草剂和杀虫剂新型种子化合物的分子设计。通过使用已作为除草剂和杀虫剂进行测试的化合物构建分类模型，并对构建的模型进行逆分析。在除草剂的分子设计中，我们使用集成学习和一种考虑八种杂草物种之间关系的解释变量表达方法，提出了186个作为除草剂的新样本。对于杀虫剂的分子设计，我们使用欠采样和集成学习来分析不平衡数据。基于大约340,000种化合物，提出了12种潜在的杀虫剂，其中2种在测试时表现出实际活性。这些结果证明了所开发的机器学习方法在快速识别新型除草剂和杀虫剂方面的潜力。
