Prediction of Chemical Biodegradability Using Support Vector Classifier Optimized with Differential Evolution

Reliable computer
models for the prediction of chemical biodegradability
from molecular descriptors and fingerprints are very important for
making health and environmental decisions. Coupling of the differential
evolution (DE) algorithm with the support vector classifier (SVC)
in order to optimize the main parameters of the classifier resulted
in an improved classifier called the DE-SVC, which is introduced in
this paper for use in chemical biodegradability studies. The DE-SVC
was applied to predict the biodegradation of chemicals on the basis
of extensive sample data sets and known structural features of molecules.
Our optimization experiments showed that DE can efficiently find the
proper parameters of the SVC. The resulting classifier possesses strong
robustness and reliability compared with grid search, genetic algorithm,
and particle swarm optimization methods. The classification experiments
conducted here showed that the DE-SVC exhibits better classification
performance than models previously used for such studies. It is a
more effective and efficient prediction model for chemical biodegradability.