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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Related Experiment Video

Updated: Mar 7, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Quantum probability ranking principle for ligand-based virtual screening.

Mohammed Mumtaz Al-Dabbagh1, Naomie Salim2, Mubarak Himmat2

  • 1Faculty of Computing, Universiti Teknologi Malaysia, Skudia, 81310, Malaysia. mohamad.aldabbagh@protonmail.com.

Journal of Computer-Aided Molecular Design
|February 22, 2017
PubMed
Summary

A new quantum probability ranking principle (QPRP) method enhances compound prioritization in drug discovery. QPRP outperforms traditional probability ranking principle (PRP) in virtual screening of molecular compounds.

Keywords:
Ligand-basedMolecular rankingQuantum mechanicsQuantum probability ranking principleRanking chemical compoundsVirtual screening

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Quantum mechanics applications

Background:

  • Chemical libraries require efficient computational methods for prioritizing compounds.
  • Virtual screening, using the probability ranking principle (PRP), is crucial for cost-effective lead drug discovery.
  • Ranking chemical compounds by biological activity probability is essential.

Purpose of the Study:

  • To develop a novel quantum mechanics-inspired ranking approach for molecular compounds.
  • To introduce the quantum probability ranking principle (QPRP) for ligand-based virtual screening (LBVS).
  • To enhance the accuracy and efficiency of virtual screening processes.

Main Methods:

  • Developed QPRP by drawing analogies between physical experiments and molecular structure ranking.
  • Integrated quantum concepts at representation, similarity estimation, and ranking levels.
  • Utilized a quantum-based similarity searching method for chemical libraries.
  • Applied QPRP to rank molecules in simulated virtual screening experiments.

Main Results:

  • QPRP demonstrated superior performance compared to the classical PRP.
  • Simulated experiments using the MDDR dataset validated QPRP's effectiveness.
  • The quantum-based approach improved the ranking of molecular chemical compounds.

Conclusions:

  • The quantum probability ranking principle (QPRP) offers a significant advancement in virtual screening.
  • QPRP provides a more effective method for prioritizing compounds in drug discovery pipelines.
  • Quantum mechanics principles can be successfully applied to enhance computational chemistry methods.