@article {Funatsu:2011:1573-4099:1,
title = "Systematic Generation of Chemical Structures for Rational Drug Design Based on QSAR Models",
journal = "Current Computer - Aided Drug Design",
parent_itemid = "infobike://ben/cad",
publishercode ="ben",
year = "2011",
volume = "7",
number = "1",
publication date ="2011-03-01T00:00:00",
pages = "1-9",
itemtype = "ARTICLE",
issn = "1573-4099",
url = "https://www.ingentaconnect.com/content/ben/cad/2011/00000007/00000001/art00001",
doi = "doi:10.2174/157340911793743556",
keyword = "Euclidean distance, Mahalanobis distance, CoMSIA, applicability domain, silico screening, centroid, LFA-1/ICAM-1 peptide inhibitors, IR spectrum, GAs, SMILES, chemometrics, structure generation, EA-based structure generation, chemical space, Applicability domain, Structure-based drug design (SBDD), Probability Density Distribution, Tanimoto coefficient, GDB (generated a database), CoMFA, N-methyl-D-aspartic acid (NMDA), hydrofluoroether (HFE), canonicalized path, Rational Drug Design, octanol-water partition coefficient, Ensemble Learning, support vector regression (SVR), GA, Kier index, acyclic hydrocarbons, WHIM, kernel-PLS, metabolic stability, linear Gaussian models, GDB-13, back propagation neural networks (BPNN), QSAR Models, QSPR, molecular design, Gaussian functions, QSAR, NMR spectrum, MOLGEN, APPLICABILITY DOMAIN (AD), lead generation, drug design, arbitrary vertices",
author = "Funatsu, Kimito and Miyao, Tomoyuki and Arakawa, Masamoto",
abstract = "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. ",
}