Hecker Evan A, Duraiswami Chaya, Andrea Tariq A, Diller David J
Department of Molecular Modeling, Pharmacopeia, Inc, CN5350, Princeton, New Jersey 08543-5350, USA.
J Chem Inf Comput Sci. 2002 Sep-Oct;42(5):1204-11. doi: 10.1021/ci020368a.
Using a data set comprised of literature compounds and structure-activity data for cyclin dependent kinase 2, several pharmacophore hypotheses were generated using Catalyst and evaluated using several criteria. The two best were used in retrospective searches of 10 three-dimensional databases containing over 1,000,000 proprietary compounds. The results were then analyzed for the efficiency with which the hypotheses performed in the areas of compound prioritization, library prioritization, and library design. First as a test of their compound prioritization capabilities, the pharmacophore models were used to search combinatorial libraries that were known to contain CDK active compounds to see if the pharmacophore models could selectively choose the active compounds over the inactive compounds. Second as a test of their utility in library design again the pharmacophore models were used to search the active combinatorial libraries to see if the key synthons were over represented in the hits from the pharmacophore searches. Finally as a test of their ability to prioritize combinatorial libraries, several inactive libraries were searched in addition to the active libraries in order to see if the active libraries produced significantly more hits than the inactive libraries. For this study the pharmacophore models showed potential in all three areas. For compound prioritization, one of the models selected active compounds at a rate nearly 11 times that of random compound selection though in other cases models missed the active compounds entirely. For library design, most of the key fragments were over represented in the hits from at least one of the searches though again some key fragments were missed. Finally, for library prioritization, the two active libraries both produced a significant number of hits with both pharmacophore models, whereas none of the eight inactive libraries produced a significant number of hits for both models.
利用一个由细胞周期蛋白依赖性激酶2的文献化合物和构效关系数据组成的数据集，使用Catalyst生成了几个药效团假设，并使用多个标准进行评估。将两个最佳假设用于对包含超过100万种专利化合物的10个三维数据库进行回顾性搜索。然后分析结果，以考察这些假设在化合物优先级排序、库优先级排序和库设计方面的执行效率。首先，作为对其化合物优先级排序能力的测试，药效团模型被用于搜索已知包含CDK活性化合物的组合库，以查看药效团模型是否能够在活性化合物和非活性化合物中选择性地选择活性化合物。其次，作为对其在库设计中效用的测试，药效团模型再次被用于搜索活性组合库，以查看关键合成子在药效团搜索命中结果中是否过度代表。最后，作为对其对组合库进行优先级排序能力的测试，除了活性库之外还搜索了几个非活性库，以查看活性库产生的命中结果是否明显多于非活性库。在本研究中，药效团模型在所有三个方面都显示出潜力。对于化合物优先级排序，其中一个模型选择活性化合物的速率几乎是随机选择化合物速率的11倍，不过在其他情况下，模型完全错过了活性化合物。对于库设计，大多数关键片段在至少一次搜索的命中结果中过度代表，不过同样也有一些关键片段被遗漏。最后，对于库优先级排序，两个活性库在两个药效团模型搜索中都产生了大量命中结果，而八个非活性库在两个模型搜索中都没有产生大量命中结果。
