Sheridan R P, SanFeliciano S G, Kearsley S K
Department of Molecular Systems, Merck Research Laboratories, P.O.B. 2000, Rahway, NJ 07065, USA.
J Mol Graph Model. 2000 Aug-Oct;18(4-5):320-34, 525. doi: 10.1016/s1093-3263(00)00060-7.
In combinatorial synthesis, molecules are assembled by linking chemically similar fragments. Because the number of available chemical fragments often greatly exceeds the number that can be used in one synthetic experiment, one needs a rational method for choosing a subset of desirable fragments. If a combinatorial library is to be targeted against a particular biological activity, virtual screening methods can be used to predict which molecules in a virtual library are most likely to be active. When the number of possible molecules in a virtual library is very large, genetic algorithms (GAs) or simulated annealing can be used to quickly find high-scoring molecules by sampling a small subset of the total combinatorial space. We previously demonstrated how a GA can be used to select a subset of fragments for a combinatorial library, and we used topology-based methods of scoring. Here we extend that earlier work in three ways. (1) We demonstrate use of the GA with 3D scoring methods developed in our laboratory. (2) We show that the approach of assembling libraries from fragments in high-scoring molecules is a reasonable one. (3) We compare results from a library-based GA to those from a molecule-based GA.
在组合合成中，分子通过连接化学性质相似的片段进行组装。由于可用化学片段的数量常常大大超过一次合成实验中能够使用的数量，因此需要一种合理的方法来选择理想片段的子集。如果组合文库旨在针对特定的生物活性，虚拟筛选方法可用于预测虚拟文库中的哪些分子最有可能具有活性。当虚拟文库中可能的分子数量非常大时，遗传算法（GA）或模拟退火可用于通过对整个组合空间的一小部分进行采样来快速找到高分值分子。我们之前展示了如何使用遗传算法为组合文库选择片段子集，并且我们使用了基于拓扑的评分方法。在此，我们从三个方面扩展了早期的工作。（1）我们展示了遗传算法与我们实验室开发的三维评分方法的结合使用。（2）我们表明从高分值分子中的片段组装文库的方法是合理的。（3）我们将基于文库的遗传算法的结果与基于分子的遗传算法的结果进行比较。
