Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations.
The utility of genetic neural network (GNN) to obtain quantitative structure-activity relationships (QSAR) from molecular similarity matrices is described. In this application, the corticosteroid-binding globulin (CBG) binding affinity of the well-known steroid data set is examined. Excellent predictivity can be obtained through the use of either electrostatic or shape properties alone. Statistical validation using a standard randomization test indicates that the results are not due to chance correlations. Application of GNN on the combined electrostatic and shape matrix produces a six-descriptor model with a cross-validated r2 value of 0.94. The model is superior to those obtained from partial least-squares and genetic regressions, and it also compares favorably with the results for the same data set from other established 3D QSAR methods. The theoretical basis for the use of molecular similarity in QSAR is discussed.
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Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA.
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D000465:Algorithms; D008956:Models, Chemical; D015394:Molecular Structure; D016571:Neural Networks, Computer; D011485:Protein Binding; D055672:Static Electricity; D013256:Steroids / Q000737:chemistry / Q000378:metabolism*; D013329:Structure-Activity Relationship; D014156:Transcortin / Q000737:chemistry / Q000378:metabolism*
D013256:Steroids; D014156:Transcortin