化合物的有效虚拟筛选和跳架方法。

Nikil Wale, G. Karypis, Ian A. Watson
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引用次数: 9

摘要

筛选大型数据库以检索具有理想生物活性特性的结构多样化化合物的方法在药物发现和开发过程中至关重要。本文提出了一套这样的方法,旨在寻找结构上与特定查询化合物不同的化合物,同时保留其生物活性特性(支架啤酒花)。这些方法利用各种间接方法来测量查询和化合物之间的相似性,这些方法考虑了基于结构的相似性之外的其他信息。本文提出了两套技术,利用基于自动关联反馈的方法和基于分析由查询和数据库化合物形成的相似性网络的方法来捕获这些间接相似性。实验评估表明,这些方法中的许多方法在识别结构多样的活性化合物以及一般活性化合物的能力方面都大大优于先前开发的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method for effective virtual screening and scaffold-hopping in chemical compounds.
Methods that can screen large databases to retrieve a structurally diverse set of compounds with desirable bioactivity properties are critical in the drug discovery and development process. This paper presents a set of such methods, which are designed to find compounds that are structurally different to a certain query compound while retaining its bioactivity properties (scaffold hops). These methods utilize various indirect ways of measuring the similarity between the query and a compound that take into account additional information beyond their structure-based similarities. Two sets of techniques are presented that capture these indirect similarities using approaches based on automatic relevance feedback and on analyzing the similarity network formed by the query and the database compounds. Experimental evaluation shows that many of these methods substantially outperform previously developed approaches both in terms of their ability to identify structurally diverse active compounds as well as active compounds in general.
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