Neural networks as a tool to classify compounds according to aromaticity criteria

11 pages, 8 figures, 3 tables.-- PMID: 17323387 [PubMed].-- Supporting information available: Correlations for all compounds, Correlations for compounds classified as aromatic by the Kohonen network, Factor Analysis for all compounds (N = 107), Factor Analysis for compounds classified as aromatic by the Kohonen network (N = 72), T-Test between NICS-NICS(1) for all compounds, T-Test between NICS-NICS(1) for compounds classified as aromatic by the Kohonen network, 9 pages. Available at: http://www.wiley-vch.de/contents/jc_2111/2007/f601101_s.pdfAromaticity is a fundamental concept in chemistry, with many theoretical and practical implications. Although most organic compounds can be categorized as aromatic, non-aromatic, or antiaromatic, it is often difficult to classify borderline compounds as well as to quantify this property. Many aromaticity criteria have been proposed, although none of them gives an entirely satisfactory solution. The inability to fully arrange organic compounds according to a single criterion arises from the fact that aromaticity is a multidimensional phenomenon. Neural networks are computational techniques that allow one to treat a large amount of data, thereby reducing the dimensionality of the input set to a bidimensional output. We present the successful applications of Kohonen's self-organizing maps to classify organic compounds according to aromaticity criteria, showing a good correlation between the aromaticity of a compound and its placement in a particular neuron. Although the input data for the training of the network were different aromaticity criteria (stabilization energy, diamagnetic susceptibility, NICS, NICS(1), and HOMA) for five-membered heterocycles, the method can be extended to other organic compounds. Some useful features of this method are: 1) it is very fast, requiring less than one minute of computational time to place a new compound in the map; 2) the placement of the different compounds in the map is conveniently visualized; 3) the position of a compound in the map depends on its aromatic character, thus allowing us to establish a quantitative scale of aromaticity, based on Euclidean distances between neurons, 4) it has predictive power. Overall, the results reported herein constitute a significant contribution to the longstanding debate on the quantitative treatment of aromaticity.This work was financially supported by MEyC (CTQ2004-01978). M.A. thanks MEyC for a fellowship. We also thank the Centro de Supercomputación de Galicia (CESGA) for computational time at the COMPAQ HPC 320 supercomputer.Peer reviewe