Ultrafast shape recognition: Evaluating a new ligand-based virtual screening technology
Ultrafast shape recognition: Evaluating a new ligand-based virtual screening technology
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DOI：
10.1016/j.jmgm.2009.01.001
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发表时间：
2009-04-01
影响因子：
2.9
通讯作者：
Richards, W. Graham
中科院分区：
文献类型：
--
作者：
Ballester, Pedro J.;Finn, Paul W.;Richards, W. Graham
大规模数据库搜索以鉴定对感兴趣的靶标具有共同生物活性的分子被广泛用于药物发现。这种努力需要编码指示生物活性的分子特性的方法的可用性和至少一种用作模板的活性分子。分子形状已被证明是生物活性的重要指标;然而，目前使用的方法相对较慢，因此非常需要更快和更可靠的方法。最近，一种新的基于非叠加的分子形状比较方法，称为超快形状识别（USR），已经设计出计算性能比以前现有的方法快至少三个数量级。在这项研究中，我们调查的性能USR在检索生物活性化合物，通过回顾性虚拟筛选实验。结果表明，USR的平均性能优于市售的形状相似性方法，同时筛选构象的速度快2500倍以上。这种出色的计算性能对于搜索比以前可能的化学空间大得多的部分特别有用，这使得USR成为寻找药物发现计划新先导分子的非常有价值的新工具。(C)2009爱思唯尔公司All rights reserved.
Large scale database searching to identify molecules that share a common biological activity for a target of interest is widely used in drug discovery. Such an endeavour requires the availability of a method encoding molecular properties that are indicative of biological activity and at least one active molecule to be used as a template. Molecular shape has been shown to be an important indicator of biological activity; however, currently used methods are relatively slow, so faster and more reliable methods are highly desirable. Recently, a new non-superposition based method for molecular shape comparison, called Ultrafast Shape Recognition (USR), has been devised with computational performance at least three orders of magnitude faster than previously existing methods. In this study, we investigate the performance of USR in retrieving biologically active compounds through retrospective Virtual Screening experiments. Results show that USR performs better on average than a commercially available shape similarity method, while screening conformers at a rate that is more than 2500 times faster. This outstanding computational performance is particularly useful for searching much larger portions of chemical space than previously possible, which makes USR a very valuable new tool in the search for new lead molecules for drug discovery programs. (C) 2009 Elsevier Inc. All rights reserved.