Abstract
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.
Highlights
Virtual screening refers to the use of a computer-based method to process compounds from a library or database of compounds in order to identify and select the ones that are likely to possess a desired biological activity, such as the ability to inhibit the action of a particular therapeutic target
The experimental results on MDL drug data report (MDDR)-DS1, MDDR-DS2, MDDR-DS3, maximum unbiased validation (MUV), and Directory of Useful Decoys (DUD) are presented in Tables 6–10 respectively, using cut offs at 1% and 5%
The recall values of MDDR-DS1, MDDR-DS2 and MDDR-DS3 that reported in Tables 6–8 respectively showed that the proposed Standard Quantum-Based (SQB) method which deals with complex Hilbert space are obviously superior to TAN especially at cutoff 1% for DS1–DS3, and for DS1 and DS2 at cutoff 5%
Summary
Virtual screening refers to the use of a computer-based method to process compounds from a library or database of compounds in order to identify and select the ones that are likely to possess a desired biological activity, such as the ability to inhibit the action of a particular therapeutic target. Many virtual screening (VS) approaches have been implemented for searching chemical databases, such as substructure search, similarity, docking and QSAR [3]. Similarity searching is the simplest, and one of the most widely-used techniques for ligand-based virtual screening (LBVS) [4]. The increasing of the importance of similarity searching applications is due to its role in lead optimization in drug discovery programs, where the nearest neighbors for an initial lead compound are sought in order to find better compounds.
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