Classification of anti-HIV compounds using counterpropagation artificial neural networks and decision trees

Abstract

The main aim of the present work was to collect and categorize anti-HIV molecules in order to identify general structure–activity relationships. In this respect, a total of 5580 drugs and drug-like molecules was collected from 256 different articles published between 1992 and 2010. An algorithm called genetic algorithm–pattern search counterpropagation artificial neural networks (GPS-CPANN) was proposed for the classification of compounds. In addition, the CART (classification and regression trees) method was used for construction of decision trees and finding the best molecular descriptors. The results revealed that the developed CPANN models and decision tree can correctly classify the molecules according to their inhibition mechanisms and activities. Some general parameters such as molecular weight, average molecular weight, number of hydrogen atoms and number of hydroxyl groups were found to be important for describing the inhibition behaviour of anti-HIV agents. The developed classifier models in this work can be used to screen large libraries of compounds to identify those likely to display activity as anti-HIV agents.

Keywords:

• acquired immune deficiency syndrome
• classification
• counterpropagation artificial neural networks
• general structure-activity relationship
• human immunodeficiency virus type 1