Institute of Chemistry Timisoara of the Romanian Academy, 24 Mihai Viteazul Av., 300223, Timisoara, Romania.
Natural Science Laboratory, Toyo University, 5-28-20 Hakusan, Bunkyo-ku, Tokyo, 112-8606, Japan.
Environ Sci Pollut Res Int. 2019 May;26(14):14547-14561. doi: 10.1007/s11356-019-04662-9. Epub 2019 Mar 14.
Neonicotinoids are the fastest-growing class of insecticides successfully applied in plant protection, human and animal health care. The significant resistance increases led to the urgent need for alternative new neonicotinoids, with improved insecticidal activity. We performed molecular docking to describe a common binding mode of neonicotinoids into the nicotinic acetylcholine receptor, and to select the appropriate conformations to derive models. These were further used in a QSAR study employing both linear and nonlinear approaches to model the inhibitory activity against the Cowpea aphids. Linear modeling was performed by multiple linear regression and partial least squares and nonlinear modeling by artificial neural networks and support vector machine methods. The OECD principles were considered for QSAR models validation. Robust models with predictive power were found for neonicotinoid diverse structures. Based on our QSAR and docking outcomes, five new insecticides were predicted, according to the model applicability domain, the ligand efficiencies, and the binding mode. Therefore, the developed models can be confidently used for the prediction of the insecticidal activity of new chemicals, saving a substantial amount of time and money and, also, contributing to the chemical risk assessment.
新烟碱类杀虫剂是在植物保护、人类和动物保健中成功应用的增长最快的杀虫剂类别。显著的抗药性增加导致迫切需要替代新的新烟碱类杀虫剂，以提高杀虫活性。我们进行了分子对接，以描述新烟碱类杀虫剂进入烟碱型乙酰胆碱受体的常见结合模式，并选择合适的构象来推导模型。这些模型进一步用于 QSAR 研究，采用线性和非线性方法来模拟对豇豆蚜虫的抑制活性。线性建模通过多元线性回归和偏最小二乘法进行，非线性建模通过人工神经网络和支持向量机方法进行。OECD 原则被认为是 QSAR 模型验证的基础。对于新烟碱类杀虫剂的各种结构，发现了具有预测能力的稳健模型。根据模型适用性域、配体效率和结合模式，从 QSAR 和对接结果中预测了 5 种新的杀虫剂。因此，开发的模型可以自信地用于预测新化学物质的杀虫活性，节省大量的时间和金钱，也有助于化学风险评估。
