Bio-Inspired Algorithms Applied to Molecular Docking Simulations

被引：58
作者：
Heberle, G. [1 ,2 ]
de Azevedo, W. F., Jr. [1 ,3 ]
机构：
[1] Pontificia Univ Catolica Rio Grande do Sul, Inst Nacl Ciencia & Tecnol TB, Programa Posgrad Biol Celular & Mol, Fac Biociencias,Lab Bioquim Estrutural, BR-90619900 Porto Alegre, RS, Brazil
[2] Ctr Univ UNIVATES, Ctr Ciencias Biol & Saude, BR-95900000 Lajeado, RS, Brazil
[3] Pontificia Univ Catolica Rio Grande do Sul, Programa Posgrad Med & Ciencias Saude, Porto Alegre, RS, Brazil
来源：
CURRENT MEDICINAL CHEMISTRY | 2011年 / 18卷 / 09期
关键词：
Bio-inspired computing; evolutionary algorithms; molecular docking; structure-based virtual screening; protein-ligand; docking; InhA; PNP; EPSP synthase; PURINE NUCLEOSIDE PHOSPHORYLASE; PROTEIN-LIGAND DOCKING; EMPIRICAL SCORING FUNCTIONS; HUMAN-GENOME-PROJECT; INCREMENTAL CONSTRUCTION ALGORITHM; LAMARCKIAN GENETIC ALGORITHM; GENERIC EVOLUTIONARY METHOD; MYCOBACTERIUM-TUBERCULOSIS; CRYSTAL-STRUCTURE; SHIKIMATE-KINASE;
D O I：
10.2174/092986711795029573
中图分类号：
Q5 [生物化学]; Q7 [分子生物学];
学科分类号：
071010 ; 081704 ;
摘要：
Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.
引用
收藏
页码：1339 / 1352
页数：14
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