Recent Studies of QSAR on Inhibitors of Estrogen Receptor and Human Eosinophil Phosphodiesterase
Quantitative structure-activity relationships (QSARs) techniques are routinely used in modern computer-aided drug design. In this review, the common generalization and computational procedures of QSAR methods (CoMFA, CoMSIA, and HQSA) are described in detail. The predictive ability of CoMFA and CoMSIA models depends directly on the quality of molecular alignment, the selection of probe, the difference of steps and type of charge. Moreover, it is worth noting that the active conformation plays a key role in molecular alignment, and it is a very difficult task to select the active conformations for drug molecules. The approaches to determine the active conformation are also reviewed. Furthermore, strategies including the selection of different fields and the chemometric methods for QSAR model to improve predictive capabilities between the structures of drugs and the biological activities are suggested in this review. The predictive ability of HQSAR models is directly dependent on the hologram length, fragment size, and distinction parameters. By using these techniques, our recent case studies of QSAR on two categories of drugs are presented. One is a series of the inhibitors of estrogen receptor (ER), 3-arylquinazolinethione derivatives, which is a key drug target for the treatment of osteoporosis and breast cancer. The other is a series of inhibitors of human eosinophil phosphodiesterase, 5,6- dihydro-(9H)-pyrazolo[3,4-c]-1,2,4- triazolo [4,3-a]pyridines, which is a drug target for the treatment of inflammation. Satisfactory models of QSAR (with high predictive ability) on two categories of drugs were obtained with optimized parameters. According to the models of QSAR obtained in our study, new drug molecules with higher activity were proposed.
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Document Type: Research Article
Publication date: September 1, 2009
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- Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, etc., providing excellent rationales for drug development.
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