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Systematic Generation of Chemical Structures for Rational Drug Design Based on QSAR Models

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The first step in the process of drug development is to determine those lead compounds that demonstrate significant biological activity with regard to a target protein. Because this process is often costly and time consuming, there is a need to develop efficient methodologies for the generation of lead compounds for practical drug design. One promising approach for determining a potent lead compound is computational virtual screening. The biological activities of candidate structures found in virtual libraries are estimated by using quantitative structure activity relationship (QSAR) models and/or computational docking simulations. In virtual screening studies, databases of existing drugs or natural products are commonly used as a source of lead candidates. However, these databases are not sufficient for the purpose of finding lead candidates having novel scaffolds. Therefore, a method must be developed to generate novel molecular structures to indicate high activity for efficient lead discovery. In this paper, we review current trends in structure generation methods for drug design and discuss future directions. First, we present an overview of lead discovery and drug design, and then, we review structure generation methods. Here, the structure generation methods are classified on the basis of whether or not they employ QSAR models for generating structures. We conclude that the use of QSAR models for structure generation is an effective method for computational lead discovery. Finally, we discuss the problems regarding the applicability domain of QSAR models and future directions in this field.

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Keywords: APPLICABILITY DOMAIN (AD); Applicability domain; CoMFA; CoMSIA; EA-based structure generation; Ensemble Learning; Euclidean distance; GA; GAs; GDB (generated a database); GDB-13; Gaussian functions; IR spectrum; Kier index; LFA-1/ICAM-1 peptide inhibitors; MOLGEN; Mahalanobis distance; N-methyl-D-aspartic acid (NMDA); NMR spectrum; Probability Density Distribution; QSAR; QSAR Models; QSPR; Rational Drug Design; SMILES; Structure-based drug design (SBDD); Tanimoto coefficient; WHIM; acyclic hydrocarbons; applicability domain; arbitrary vertices; back propagation neural networks (BPNN); canonicalized path; centroid; chemical space; chemometrics; drug design; hydrofluoroether (HFE); kernel-PLS; lead generation; linear Gaussian models; metabolic stability; molecular design; octanol-water partition coefficient; silico screening; structure generation; support vector regression (SVR)

Document Type: Research Article

Publication date: March 1, 2011

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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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