Very large virtual compound spaces: construction, storage and utility in drug discovery.
Very large virtual compound spaces: construction, storage and utility in drug discovery.
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DOI：
10.1016/j.ddtec.2013.01.004
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发表时间：
2013-09-01
期刊：
影响因子：
--
通讯作者：
Peng, Zhengwei
中科院分区：
文献类型：
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作者：
Peng, Zhengwei
本报告回顾了最近在建造、存储和探索超大型虚拟复合空间方面的活动。正如预期的那样，以最高分辨率（单个原子和键）系统地探索化合物空间本质上是棘手的。相比之下，通过停留在有限数量的反应和有限数量的反应物或片段内，已经以组合的方式构建了几个虚拟化合物空间，其大小范围从10（11）11到10（20）20化合物。已经开发了多种搜索方法来执行搜索（例如相似性，精确和子结构）到这些复合空间，而不需要完全枚举。在构建其中一些虚拟化合物空间期间，用于合成可行性的前期投资使药物化学家能够更广泛地采用设计和合成用于药物发现的重要化合物。最近的活动，通过基于遗传算法的进化方法探索虚拟化合物空间的领域也表明了积极的重点转移，从方法开发的工作流程，集成和易用性，所有这些都需要这种方法被广泛采用的药物化学家。
Recent activities in the construction, storage and exploration of very large virtual compound spaces are reviewed by this report. As expected, the systematic exploration of compound spaces at the highest resolution (individual atoms and bonds) is intrinsically intractable. By contrast, by staying within a finite number of reactions and a finite number of reactants or fragments, several virtual compound spaces have been constructed in a combinatorial fashion with sizes ranging from 10(11)11 to 10(20)20 compounds. Multiple search methods have been developed to perform searches (e.g. similarity, exact and substructure) into those compound spaces without the need for full enumeration. The up-front investment spent on synthetic feasibility during the construction of some of those virtual compound spaces enables a wider adoption by medicinal chemists to design and synthesize important compounds for drug discovery. Recent activities in the area of exploring virtual compound spaces via the evolutionary approach based on Genetic Algorithm also suggests a positive shift of focus from method development to workflow, integration and ease of use, all of which are required for this approach to be widely adopted by medicinal chemists.