Qspr Study Of The Water Solubility Of A Diverse Set Of Agrochemicals: Hybrid (ga/ Mlr) Approach

A quantitative structure- property relationship (QSPR) was performed for the prediction of the aqueous solubility of pesticides belonging to four chemical classes: acid, urea, triazine, and carbamate. The entire set of 77 pesticides was divided into a training set of 58 pesticides and a test set of 19 pesticides according to the Snee technique. A six descriptor model, with squared correlation coefficient (R2) of 0.8895 and standard error of estimation (s) of 0.52 log unit, was developed by applying multiple linear regression analysis using the ordinary least square regression method and genetic algorithm- variable subset selection. The reliability of the proposed model was further illustrated using various evaluation techniques: leave- one- out cross- validation, bootstrap, randomization tests, and validation through the test set