A Quantum-Based Similarity Method in Virtual Screening
A Quantum-Based Similarity Method in Virtual Screening
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DOI：
10.3390/molecules201018107
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发表时间：
2015-10-01
期刊：
影响因子：
4.6
通讯作者：
Saeed, Faisal
中科院分区：
文献类型：
--
作者：
Al-Dabbagh, Mohammed Mumtaz;Salim, Naomie;Saeed, Faisal
基于配体的虚拟筛选最广泛使用的技术之一是相似性搜索。本研究采用量子力学的概念，提出了受量子理论启发的最先进的分子相似性方法。分子化合物在数学量子空间中的表示对基于量子相似性方法的发展起着至关重要的作用。量子理论的关键概念之一是复数的使用。因此，本研究提出三种不同的技术来嵌入和重新表示的分子化合物，以符合复数格式。在这项研究中开发的基于量子的相似性方法依赖于分子的复杂纯希尔伯特空间，称为标准量子基（SQB）。检索到的活性分子的召回率在前1%和前5%，并使用显著性检验来评估我们提出的方法。使用MDL药物数据报告（MDDR）、最大无偏验证（MUV）和有用诱饵目录（DUD）数据集进行实验，并用二维指纹图表示。模拟的虚拟筛选实验表明，SQB方法的有效性显著提高，这是由于分子化合物在复数形式中的代表性作用，与Tanimoto基准相似性度量相比。
One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB). The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure.