QSAR modeling the toxicity of pesticides against Americamysis bahia. (November 2020) - Yang, Lu; Wang, Yinghuan; Chang, Jing; Pan, Yifan; Wei, Ruojin; Li, Jianzhong; Wang, Huili
Abstract: The widespread use of pesticides has received increasing attention in regulatory agencies because their extensive overuse and various adverse effects on all living organisms. Organizations such as EPA and ECHA have published laws that pesticides should be fully evaluated before bring them to market. In the present study, we evaluated the pesticides toxicity using the Quantitative Structural-Activity Relationship (QSAR) method. The models for the single class pesticides (herbicides, insecticides and fungicides) as well as the general class pesticides (the combined dataset plus some microbicides, molluscicides, etc.) were developed using the Genetic Algorithm and Multiple Linear Regression method. The internal and external validation results suggested that all the obtained models were stable and predictive. According to the modeling descriptors, the lipophilic descriptors contributed positively while all the electrotopological state descriptors showed a negative contribution, their presences in every model verified the conspicuous influence of molecular lipophilicity and hydrophilicity on the pesticides toxicity. However, the influence of topological structure descriptors was different and varies with the physiochemical information they encode. Finally, the models presented in this paper would help assess the pesticides toxicity against Americamysis bahia, shorten test time, and reduce the cost of pesticides risk assessment. Graphical abstract: Image 1 Highlights: The toxicity of pesticides against Americamysis bahia has been assessed using the QSAR method. All the models were developed based on the well-defined and easy-interpretable 2D descriptors. The obtained models were strictly validated with various internal and external metrics. Molecular lipophilicity and hydrophilicity impacted the pesticide toxicity the most. The obtained models would help fill data gaps, save test time, and reduce the cost of pesticides risk assessment.