Cho S J, Zheng W, Tropsha A
Laboratory for Molecular Modeling, School of Pharmacy, University of North Carolina, Chapel Hill 27599-7360, USA.
Pac Symp Biocomput. 1998:305-16.
A strategy for rational design of targeted combinatorial libraries is described. The aim of this approach is to select a subset of available building blocks for the library synthesis that are most likely to be present in the active compounds. Building blocks that are used in the underlying combinatorial chemical reaction are randomly assembled to produce virtual combinatorial library compounds, which are represented by various chemical descriptors. Stochastic algorithms (simulated annealing, genetic algorithms, neural net methods) are used to search the potentially large structural space of virtual chemical libraries in order to identify compounds similar to lead compound(-s). The selection of a virtual molecule as a candidate for the targeted library is based either on its chemical similarity to a biologically active probe or on its biological activity predicted from a pre-constructed QSAR equation. Frequency analysis of building block composition of the selected virtual compounds identifies building blocks that can be used in combinatorial synthesis of chemical libraries with high similarity to the lead compound(-s). This method is applied to rational design of the library with bradykinin potentiating activity. Twenty eight bradykinin potentiating pentapeptides were used as a training set for the development of a QSAR equation, and, alternatively, two active pentapeptides, VEWAK and VKWAP, were used as probe molecules. In each case, the frequency distribution of amino acids in the top 100 peptides suggested by the method resembles the frequency distribution of amino acids found in the active peptides. The results obtained after GA optimization also compared favorably with those obtained by the exhaustive analysis of all possible 3.2 millions pentapeptides.
本文描述了一种用于合理设计靶向组合文库的策略。该方法的目的是为文库合成选择一组最有可能存在于活性化合物中的可用构建模块。在基础组合化学反应中使用的构建模块被随机组装以生成虚拟组合文库化合物，这些化合物由各种化学描述符表示。使用随机算法（模拟退火、遗传算法、神经网络方法）搜索虚拟化学文库潜在的巨大结构空间，以识别与先导化合物相似的化合物。选择虚拟分子作为靶向文库的候选物，要么基于其与生物活性探针的化学相似性，要么基于从预先构建的定量构效关系（QSAR）方程预测的其生物活性。对所选虚拟化合物的构建模块组成进行频率分析，可识别可用于化学文库组合合成且与先导化合物高度相似的构建模块。该方法应用于具有缓激肽增强活性的文库的合理设计。使用28种缓激肽增强五肽作为开发QSAR方程的训练集，或者使用两种活性五肽VEWAK和VKWAP作为探针分子。在每种情况下，该方法建议的前100种肽中氨基酸的频率分布类似于活性肽中发现的氨基酸频率分布。遗传算法（GA）优化后获得的结果也与对所有可能的320万个五肽进行详尽分析所获得的结果相比更具优势。
