Prediction of glycine/NMDA receptor antagonist inhibition from molecular structure

Abstract

The design and blood brain barrier crossing of glycine/NMDA receptor antagonists are of significant interest in pharmaceutical research. The use of these antagonists in stroke or seizure reduction have been considered. Measuring the inhibitory concentrations, however, can be time-consuming and costly. The use of quantitative structure-activity relationships to estimate IC(50) values for these receptor antagonists is an attractive alternative compared to experimental measurement. A data set of 109 compounds with measured log(IC(50)) values ranging from -0.57 to 4.5 is used. Structural information is encoded with numerical descriptors for topological, electronic, geometric, and polar surface properties. A genetic algorithm with a computational neural network fitness evaluator is used to select the best descriptor subsets. Multiple linear regression and computational neural network models are developed. Additionally, a quantitative radial basis function neural network (QRBFNN) was developed with the intent of introducing nonlinearity at a faster speed. A genetic algorithm using the radial basis function network as a fitness evaluator was also developed to search descriptor space for optimum subsets. All models are tested using an external prediction set. The nonlinear computational neural network model has root-mean-square errors of approximately half a log unit.