title: The comparative molecular surface analysis (COMSA): a novel tool for molecular design.
abstract: A new method allowing for 3-D QSAR analysis and the prediction of biological activity is presented. Unlike comparative molecular field analysis (CoMFA)-like techniques, it is based not on a comparison of the properties characterizing a discrete set of points but on the mean electrostatic potential (MEP) calculated and labeling specific areas defined on the molecular surface. A Kohonen self-organizing neural network and partial least square (PLS) analysis have been used for performing such an operation. The series of steroids complexing the corticosteroid (CBG) and testosterone (TBG) globulins, which forms a benchmark measuring the performance of the methods in molecular design, and a series of benzoic acids described by the Hammett sigma constants is used for testing the method. It is demonstrated that a method can be used efficiently to evaluate the responses determined both by the combination of electrostatic and steric effects or by electrostatic effects alone, therefore, two different schemes were developed. The first one, which involves PLS analysis of the full comparative networks, covers both steric and electrostatic effects. This scheme works well for both the CBG and TBG data. The second scheme takes into account only the properties (MEP) of these regions within molecules that can be superimposed with the template molecule. This scheme provides the best predictive power for the benzoic acids series. Comparison of the results from a CoMFA analysis proves that method is at least as effective for the responses limited by electrostatic effects, although it significantly outperforms CoMFA for CBG affinity which is dominated by steric effects.
authors.affiliation: Institute of Chemistry, University of Silesia, Katowice, Poland. polanski@us.edu.pl
authors.forename: J
authors.initials: J
authors.lastname: Polanski
authors.forename: B
authors.initials: B
authors.lastname: Walczak
mesh_terms: D001565:Benzoates / Q000737:chemistry; D017076:Computer-Aided Design; D015195:Drug Design; D016018:Least-Squares Analysis; D008958:Models, Molecular; D016571:Neural Networks, Computer; D012738:Sex Hormone-Binding Globulin / Q000737:chemistry; D055672:Static Electricity; D013256:Steroids / Q000737:chemistry; D013329:Structure-Activity Relationship; D013499:Surface Properties; D014156:Transcortin / Q000737:chemistry
chemical_list: D001565:Benzoates; D012738:Sex Hormone-Binding Globulin; D013256:Steroids; D014156:Transcortin