QSAR and subsequent molecular design are very important steps in drug discovery. Through QSAR, one derives a model that relates a set of molecular descriptors to a biological activity. The resulting model can be used to predict the activity values of new compounds in molecular design. QSAR models range from simple, parametric equations to complex, non-linear models. These models have each specific advantage and shortcoming derived from their own algorithms. We have developed hybrid approaches combining GA, multiway PLS and NN to utilize specific advantage and to cover specific shortcoming of each method. We have picked up five topics and outlined with the representative examples in this review article.
Keywords: quantitative structure-activity relationship, molecular design, multi-way partial least squares, genetic algorithm, neural network, molecular surface, molecular alignment
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