title: Quantum probability ranking principle for ligand-based virtual screening.
abstract: Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.
authors.affiliation: Faculty of Computing, Universiti Teknologi Malaysia, Skudia, 81310, Malaysia. mohamad.aldabbagh@protonmail.com.
authors.forename: Mohammed Mumtaz
authors.initials: MM
authors.lastname: Al-Dabbagh
authors.affiliation: Faculty of Computing, Universiti Teknologi Malaysia, Skudia, 81310, Malaysia.
authors.forename: Naomie
authors.initials: N
authors.lastname: Salim
authors.affiliation: Faculty of Computing, Universiti Teknologi Malaysia, Skudia, 81310, Malaysia.
authors.forename: Mubarak
authors.initials: M
authors.lastname: Himmat
authors.affiliation: Faculty of Engineering, Karary University, Khartoum, 12304, Sudan.
authors.forename: Ali
authors.initials: A
authors.lastname: Ahmed
authors.affiliation: Faculty of Computing, Universiti Teknologi Malaysia, Skudia, 81310, Malaysia.
authors.forename: Faisal
authors.initials: F
authors.lastname: Saeed
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D011336:Probability; D011506:Proteins / Q000378:metabolism; D011789:Quantum Theory; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000494:pharmacology
chemical_list: D008024:Ligands; D011506:Proteins; D054852:Small Molecule Libraries
keywords: Ligand-based; Molecular ranking; Quantum mechanics; Quantum probability ranking principle; Ranking chemical compounds; Virtual screening