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Determination of Crystal Structures01:29

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In the late 1800s, the revelation that light extended beyond visible wavelengths led to the discovery of X-rays by Wilhelm Roentgen. Recognized as high-energy electromagnetic radiation with short wavelengths, X-rays prompted exploration into their interaction with crystals. Max von Laue proposed in 1912 that the periodic arrangement of atoms, ions, or molecules in crystals would cause them to diffract X-rays, a hypothesis confirmed through experiments with copper sulfate and zinc sulfide...

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Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach.

Suman Kumar Mandal1, Parthapratim Munshi1

  • 1Chemical and Biological Crystallography, Department of Chemistry, School of Natural Sciences, Shiv Nadar University, Dadri 201314, Uttar Pradesh, India.

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Summary

This study introduces the quantum crystallographic approach-counterpoise corrected kernel energy method (KEM-CP) to enhance drug discovery lead optimization. KEM-CP improves prediction accuracy over traditional molecular docking, aiding virtual screening of potent drug candidates.

Keywords:
kernel energy methodlead structuremolecular dockingprotein-ligand interactionquantum crystallographyscoring function

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Structural biology

Background:

  • Lead structure optimization is vital in drug discovery.
  • Traditional molecular docking methods face accuracy limitations.
  • Accurate prediction of ligand binding is essential for efficient drug development.

Purpose of the Study:

  • To evaluate the efficacy of the quantum crystallographic approach-counterpoise corrected kernel energy method (KEM-CP) for improving molecular docking accuracy.
  • To assess the versatility of KEM-CP across different protein targets and active site environments.
  • To demonstrate KEM-CP's potential in enhancing virtual screening for drug discovery.

Main Methods:

  • Applied KEM-CP combined with GoldScore for molecular docking.
  • Utilized protein-ligand complexes of human aldose reductase, cyclin-dependent kinase 2, and estrogen receptor β at varying resolutions.
  • Tested KEM-CP across diverse active site environments (hydrophilic to hydrophobic).

Main Results:

  • KEM-CP significantly improved prediction accuracy compared to GoldScore alone.
  • KEM-CP demonstrated independence from environmental specificity and structural resolution.
  • Ligand rankings by KEM-CP correlated well with experimental IC50 values.

Conclusions:

  • KEM-CP offers a versatile and accurate enhancement to molecular docking for drug discovery.
  • This computationally inexpensive method facilitates virtual screening of potent ligands.
  • KEM-CP is expected to accelerate drug discovery research by improving lead optimization accuracy.