A Study of Structure−Carcinogenic Potency Relationship with Artificial Neural Networks. The Using of Descriptors Related to Geometrical and Electronic Structures

Abstract

This contribution is an attempt to estimate carcinogenic potency (measured in TD50 dose) of molecules using artificial neural networks (ANN) with counterpropagation learning strategy. Three kinds of descriptors have been tested: geometrical structures of molecules, which have been described with 3D coordinates of all atoms, geometrical structures in combination with atomic charges, and energy spectra of occupied orbitals, i.e., the electronic structures. Structures or structures plus atomic charges have been represented with “spectrum-like” representation, which is suitable as input for ANN modelling. A set of 45 benzene derivatives was considered in this study. The models were able to recognize structures of training set, and a weak correlation between descriptors and carcinogenic potency was found.