Combating Diseases with Computational Strategies Used for Drug Design and Discovery

被引:15
|
作者
Makhouri, Farahnaz R. [1 ]
Ghasemi, Jahan B. [2 ]
机构
[1] KN Toosi Univ Technol, Chem Dept, Fac Sci, Tehran, Iran
[2] Univ Tehran, Sch Sci, Analyt Chem Dept, Fac Chem, Tehran, Iran
关键词
Target identification; Lead discovery and optimization; Virtual screening; Virtual docking; QSAR; Pharmacophore mapping; In silico ADMET/PBPK prediction; DE-NOVO DESIGN; PROBABILISTIC NEURAL-NETWORKS; HUMAN INTESTINAL-ABSORPTION; SILICO TARGET PREDICTION; PROTEIN INVERSE DOCKING; IN-SILICO; REVERSE DOCKING; PHARMACOPHORE MODELS; SMALL-MOLECULE; GENOME-SCALE;
D O I
10.2174/1568026619666190121125106
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Computer-aided drug discovery (CADD) tools have provided an effective way in the drug discovery pipeline for expediting of this long process and economizing the cost of research and development. Due to the dramatic increase in the availability of human proteins as drug targets and small molecule information due to the advances in bioinformatics, cheminformatics, genomics, proteomics, and structural information, the applicability of in silico drug discovery has been extended. Computational approaches have been used at almost all stages in the drug discovery pipeline including target identification and validation, lead discovery and optimization, and pharmacokinetic and toxicity profiles prediction. As each area covers a variety of computational methods, it is unmanageable to assess comprehensively all areas of CADD applications or every aspect of an area in one review article. However, in this article, we tried to present an overview of computational methods used in almost all the areas concerned with drug design and highlight some of the recent successes.
引用
收藏
页码:2743 / 2773
页数:31
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