Volume 26, Number 8, October 1998Chemometrics 98
|Page(s)||333 - 341|
Comparison of chemical databases: Analysis of molecular diversity with Self Organising Maps (SOM)P. Bernard1, A. Golbraikh1, D. Kireev1, J.R. Chrétien1 and N. Rozhkova2
1 Laboratory of Chemometrics, University of Orléans, BP. 6759, 45067 Orléans Cedex 2, France
2 Plant Protection Chemical Research Institute, Ugrezhskaya 31, 109088 Moscow, Russia
Self Organising Map (SOM), also known as Kohonen Neural Network, is tested as a non supervised procedure for comparing molecular databases. Each chemical compound being represented by a point in the hyperspace of the molecular descriptors, SOMs was used to reflect the multidimensional hyperspace onto a two dimensional (2D) map while preserving the order of distances between the points, but in a non linear way. The aim of this work was to apply SOM to the study of the overlapping of two databases in order to obtain information about the extent of their differences in regard to their molecular diversity. Firstly, the ability of SOM to discriminate between two virtual databases was investigated. The positions of these two virtual databases were made to vary from non-overlapping to overlapping ones. In any considered cases, all the individuals of these two databases are processed simultaneously to give one SOM. From this map it is possible to analyse and understand the structure of the original data. Secondly two chemical databases are compared. The first chemical database deals with the commercially available organophosphorous pesticides (OPC), the second one deals with more than two thousand OPC tested as potent pesticides. Given the biological data known for each compound, the second database was shown to bring an interesting supplement to the structural information nested in the first database taken as a reference. Furthermore, the results obtained indicate that SOM can be used for the search of new leads among available databases and the exploration of new structural domains for a given biological activity.
Key words: Kohonen neural network / self organizing map / classification / chemical databases / virtual screening / pesticide / organophosphorous compounds.
© EDP Sciences, Wiley-VCH 1998