A Novel Method for the Inverse QSAR/QSPR to Monocyclic Chemical Compounds Based on Artificial Neural Networks and Integer Programming

Quantitative structure activity/property relationship (QSAR/QSPR) analysis is a major approach for computer-aided drug design and has also been studied in bioinformatics. Recently, a novel method has been proposed for inverse QSAR/QSPR using both artificial neural networks (ANN) and mixed integer linear programming (MILP), where inverse QSAR/QSPR is to infer chemical structures from given chemical activities/properties. However, the framework has been applied only to the case of acyclic chemical compounds so far. In this paper, we significantly extend the framework and present a new method for inverse QSAR/QSPR of monocyclic chemical compounds. The results of computational experiments using such chemical properties as heat of atomization, heat of combustion, and octanol/water partition coefficient suggest that the proposed method is much more useful than the previous method.