Meta-heuristics on quantitative structure-activity relationships: study on polychlorinated biphenyls

Abstract

A genetic algorithm was developed and assessed in order to select pairs of proper structural descriptors able to estimate and predict octanol-water partition coefficients of polychlorinated biphenyls (PCBs). The molecular descriptors family was calculated for a sample of 206 PCBs. The problem of searching for the proper descriptors in order to identify structure-activity relationships was translated in genetic terms. The following parameters were imposed in the genetic algorithm (GA) search: sample size - 12, number of variables in multivariate linear regression - 4, imposed adaptation requirements - 3 criteria, maximum number of generations - 50,000, selection strategy - tournament, probability of parent/child mutation - 0.05, number of genes implied in the mutation - 2, optimization parameter - determination coefficient, optimization score - minimum in the sample, and optimization objective - maximum. The highest determination coefficient was obtained in the generation 17,277. Twenty-one evolutions were studied until the optimum solution was obtained. The model identified by the implemented genetic algorithm proved not to be statistically different from the model identified through complete search (Z(Steiger) = 1.37, p = 0.0861). According to this GA model, the relationship between the structure of PCBs and octanol-water partition coefficients was of geometric and topological nature as previously revealed by the complete search. The genetic algorithm proved its ability to identify two pairs of molecular descriptors able to characterize the relationship between the structure of PCBs and the octanol-water partition coefficient.