Molecular maps of atom-level properties (MOLMAPs) were developed to represent the diversity of chemical bonds existing in a molecule. Chemical reactivity, being related to the ability for bond breaking and bond making, is primarily determined by the properties of bonds available in a molecule. In order to use physicochemical properties of individual bonds for an entire molecule, and at the same time having a fixed-length molecular representation, all the bonds of a molecule are mapped into a fixed-size 2D self-organizing map (MOLMAP). This article illustrates the application of MOLMAP descriptors to QSAR, with a study of the radical scavenging activity of 47 naturally occurring phenolic antioxidants. Counterpropagation neural networks (CPG NNs) were trained with MOLMAP descriptors selected using genetic algorithms to predict antioxidant activity. The model Was Subsequently validated by the leave-one-out (LOO) procedure obtaining a q(2) of 0.71. Random Forests were grown with the entire set of MOLMAP descriptors giving 70% of correct classifications as potent, active or inactive in a LOO experiment. Interpretations of both models in terms of discriminant variables were concordant and allowed identifying bonds and substructures that are mostly responsible for antioxidant activity. This work shows how MOLMAPs can be used for data mining of structural and biological activity data, leading to the extraction of relationships between local properties and activity. (c) 2005 Elsevier Ltd. All rights reserved.