期刊
CURRENT DRUG SAFETY
卷 7, 期 4, 页码 289-297出版社
BENTHAM SCIENCE PUBL
DOI: 10.2174/157488612804096533
关键词
Drug discovery; in silico methods; molecular modeling; quantitative structure toxicity relationship (QSTR)
Undesirable toxicity is still a major block in the drug discovery process. Obviously, capable techniques that identify poor effects at a very early stage of product development and provide reasonable toxicity estimates for the huge number of untested compounds are needed. In silico techniques are very useful for this purpose, because of their advantage in reducing time and cost. These case studies give the description of in silico validation techniques and applied modeling methods for the prediction of toxicity of chemical compounds. In silico toxicity prediction techniques can be classified into two categories: Molecular Modeling and methods that derive predictions from experimental data. Molecular modeling is a computational approach to mimic the behavior of molecules, from small molecules (e.g. in conformational analysis) to biomolecules. But the same approaches can also be applied for toxicological purposes, if the mechanism is receptor mediated. Quantitative Structure-Toxicity Relationships (QSTRs) models are typical examples for the prediction of toxicity which relates variations in the molecular structures to toxicity. There are many applied modeling techniques in QSTR such as Partial Least Squares, Artificial Neural Networks, and Principal Component Regression (PCR). The applicability of these techniques in predictive toxicology will be discussed with different examples of sets of chemical compounds.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据