Peng Zhengwei
Drug Discov Today Technol. 2013 Sep;10(3):e387-94. doi: 10.1016/j.ddtec.2013.01.004.
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.
本报告回顾了近期在构建、存储和探索超大型虚拟化合物空间方面的活动。正如预期的那样,以最高分辨率(单个原子和键)对化合物空间进行系统探索本质上是难以处理的。相比之下,通过限定在有限数量的反应以及有限数量的反应物或片段范围内,已经以组合方式构建了几个虚拟化合物空间,其大小从10的11次方到10的20次方个化合物不等。已经开发了多种搜索方法,用于在这些化合物空间中进行搜索(例如相似性、精确和子结构搜索),而无需进行完全枚举。在构建其中一些虚拟化合物空间时,在合成可行性方面的前期投入使得药物化学家能够更广泛地采用这些空间来设计和合成用于药物发现的重要化合物。近期基于遗传算法的进化方法在探索虚拟化合物空间领域的活动也表明,关注点正从方法开发积极转向工作流程、整合以及易用性,而这些都是该方法被药物化学家广泛采用所必需的。