Abstract
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.
Keywords: Bio-inspired computing, evolutionary algorithms, molecular docking, structure-based virtual screening, protein-ligand, docking, InhA, PNP, EPSP synthase, quantum states
Current Medicinal Chemistry
Title: Bio-Inspired Algorithms Applied to Molecular Docking Simulations
Volume: 18 Issue: 9
Author(s): G. Heberle and W. F. de Azevedo
Affiliation:
Keywords: Bio-inspired computing, evolutionary algorithms, molecular docking, structure-based virtual screening, protein-ligand, docking, InhA, PNP, EPSP synthase, quantum states
Abstract: 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.
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Cite this article as:
Heberle G. and F. de Azevedo W., Bio-Inspired Algorithms Applied to Molecular Docking Simulations, Current Medicinal Chemistry 2011; 18 (9) . https://dx.doi.org/10.2174/092986711795029573
DOI https://dx.doi.org/10.2174/092986711795029573 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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