An Approach to Identify New Insecticides Against Myzus Persicae. In silico Study Based on Linear and Non‐linear Regression Techniques. Issue 8 (11th January 2019)

• Record Type: Journal Article
• Authors: Crisan, Luminita; Borota, Ana; Suzuki, Takahiro; Funar‐Timofei, Simona
• Is Part Of: Molecular informatics. Volume 38:Issue 8/9(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-01-11
• Subjects: guadipyr -- MLR -- SVM -- ANN -- pharmacophore; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201800119
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: Neonicotinoids are known to have high insecticidal potency, low mammalian toxicity and relatively tough activity for the development of resistance against aphids. A series of guadipyr insecticides, active against Myzus persicae was engaged in silico studies, based on Multiple Linear Regression (MLR), Partial Least Squares regression (PLS), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Pharmacophore modeling. Robust and predictive models were built using correlations between the insecticidal profile, expressed by experimental pLC50 values, and molecular descriptors, calculated from the energy optimized structures. Four new potential insecticides active against Myzus persicae and their predicted pLC50 toxicity values were reported for the first time. The models presented here can be used as an approach in the screening and prioritization of chemicals in a scientific and regulatory frame and for toxicity prediction. Abstract :