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基于虚拟靶点的数据库筛选中化合物选择过程中对分子量的考量。

Consideration of molecular weight during compound selection in virtual target-based database screening.

作者信息

Pan Yongping, Huang Niu, Cho Sam, MacKerell Alexander D

机构信息

Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland 21201.

出版信息

J Chem Inf Comput Sci. 2003 Jan-Feb;43(1):267-72. doi: 10.1021/ci020055f.

Abstract

Virtual database screening allows for millions of chemical compounds to be computationally selected based on structural complimentary to known inhibitors or to a target binding site on a biological macromolecule. Compound selection in virtual database screening when targeting a biological macromolecule is typically based on the interaction energy between the chemical compound and the target macromolecule. In the present study it is shown that this approach is biased toward the selection of high molecular weight compounds due to the contribution of the compound size to the energy score. To account for molecular weight during energy based screening, we propose normalization strategies based on the total number of heavy atoms in the chemical compounds being screened. This approach is computationally efficient and produces molecular weight distributions of selected compounds that can be selected to be (1) lower than that of the original database used in the virtual screening, which may be desirable for selection of leadlike compounds or (2) similar to that of the original database, which may be desirable for the selection of drug-like compounds. By eliminating the bias in target-based database screening toward higher molecular weight compounds it is anticipated that the proposed procedure will enhance the success rate of computer-aided drug design.

摘要

虚拟数据库筛选允许基于与已知抑制剂或生物大分子上的靶标结合位点的结构互补性,通过计算选择数百万种化合物。当以生物大分子为靶点进行虚拟数据库筛选时,化合物的选择通常基于化合物与靶标大分子之间的相互作用能。本研究表明,由于化合物大小对能量得分的贡献,这种方法倾向于选择高分子量化合物。为了在基于能量的筛选过程中考虑分子量,我们提出了基于被筛选化合物中重原子总数的归一化策略。这种方法计算效率高,并且所产生的所选化合物的分子量分布可以被选择为:(1)低于虚拟筛选中使用的原始数据库的分子量分布,这对于类先导化合物的选择可能是理想的;或者(2)与原始数据库的分子量分布相似,这对于类药物化合物的选择可能是理想的。通过消除基于靶点的数据库筛选中对高分子量化合物的偏向,预计所提出的程序将提高计算机辅助药物设计的成功率。

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