Evolutionary algorithms for de novo drug design - A survey
Evolutionary algorithms for de novo drug design - A survey
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DOI：
10.1016/j.asoc.2014.09.042
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发表时间：
2015-02-01
影响因子：
8.7
通讯作者：
Coumar, Mohane Selvaraj
中科院分区：
文献类型：
--
作者：
Devi, R. Vasundhara;Sathya, S. Siva;Coumar, Mohane Selvaraj
药物设计和发现的过程需要几个工年和巨大的投资。计算机辅助药物设计(CADD)技术是加速药物发现过程的一种辅助手段。新药设计是一种从巨大的化学搜索空间中识别与药物相似的新化学结构的计算机辅助设计技术，它通过优化成功药物所需的多个药学相关参数来帮助发现新药。在新药设计中，由于搜索空间非常大，可以使用软计算技术进化算法(EA)来寻找最优解，在这种情况下就是一种新药。本文对新药设计工具中使用的各种EA技术进行了详细的综述和分析，特别是在计算方面。(C)2014爱思唯尔B.V.保留所有权利。
The process of drug design and discovery demands several man years and huge investment. Computer-aided drug design (CADD) technique is an aid to speed up the drug discovery process. De novo drug design, a CADD technique to identify drug-like novel chemical structures from a huge chemical search space, helps to find new drugs by the optimization of multiple pharmaceutically relevant parameters required for a successful drug. As the search space is very large in the case of de novo drug design, evolutionary algorithm (EA), a soft computing technique can be used to find an optimal solution, which in this case is a novel drug. In this paper, various EA techniques used in de novo drug design tools are surveyed and analyzed in detail, with particular emphasis on the computational aspects. (C) 2014 Elsevier B. V. All rights reserved.