Chemical pattern recognition and multivariate analysis for QSAR studies

Abstract Chemical pattern recognition (CPR) and quantitative structure-activity relationships (QSAR) studies based on multivariate analysis and chemometric techniques are reviewed. In particular, applications of the SIMCA classification method to structure-taste problems are discussed. Cluster significance analysis (CSA) is compared with modelling powers for feature selection of asymmetric data sets. A concentric hypersphere model is used to predict candidate new sweeteners. Partial least squares (PLS) modelling methods are employed to antiarrhythmic data of phenylpyridines and fungicidal and herbicidal data of thiocarbamates, respectively. The CoMFA approach to 3-dimensional QSAR using PLS modelling is described as well. In practice, QSAR is an important branch of chemometrics and enhances rational drug design and new agent development. The chemometric techniques described in the article not only work well for QSAR but also are very helpful for solving the problems related to analytical characteristics-chemical structure relationships.

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