Grzybowski Bartosz A, Ishchenko Alexey V, Shimada Jun, Shakhnovich Eugene I
Concurrent Pharmaceuticals, 1 Broadway, 14th floor, Cambridge, Massachusetts 02142, USA.
Acc Chem Res. 2002 May;35(5):261-9. doi: 10.1021/ar970146b.
Computational methods are becoming increasingly used in the drug discovery process. In this Account, we review a novel computational method for lead discovery. This method, called CombiSMoG for "combinatorial small molecule growth", is based on two components: a fast and accurate knowledge-based scoring function used to predict binding affinities of protein-ligand complexes, and a Monte Carlo combinatorial growth algorithm that generates large numbers of low-free-energy ligands in the binding site of a protein. We illustrate the advantages of the method by describing its application in the design of picomolar inhibitors for human carbonic anhydrase.
计算方法在药物发现过程中的应用越来越广泛。在本综述中，我们介绍一种用于先导化合物发现的新型计算方法。这种方法称为CombiSMoG（“组合小分子生长”），基于两个部分：一个快速准确的基于知识的评分函数，用于预测蛋白质-配体复合物的结合亲和力；以及一个蒙特卡罗组合生长算法，该算法在蛋白质的结合位点生成大量低自由能配体。我们通过描述其在设计人碳酸酐酶皮摩尔抑制剂中的应用来说明该方法的优势。
