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Related Experiment Videos

Surface descriptors for protein-ligand affinity prediction.

Ismael Zamora1, Tudor Oprea, Gabriele Cruciani

  • 1DMPK & Bioanalytical Chemistry, AstraZeneca R & D Mölndal, S-431 83 Mölndal, Sweden. ismael.zamora@telefonica.net

Journal of Medicinal Chemistry
|December 28, 2002
PubMed
Summary

VolSurf descriptors, which model drug interactions, can predict both pharmacokinetic properties and ligand-receptor binding. This dual modeling capability aids in early drug discovery by assessing drug behavior and efficacy simultaneously.

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Drug discovery

Background:

  • Molecular descriptors from VolSurf are established for modeling pharmacokinetic properties like passive permeability.
  • The interactions quantified by VolSurf (steric, hydrophobic, hydrogen bonding) are also crucial for ligand-receptor binding.
  • This suggests VolSurf descriptors' potential applicability in modeling biological interactions.

Purpose of the Study:

  • To investigate the utility of VolSurf descriptors in modeling ligand-receptor binding.
  • To assess if VolSurf can simultaneously model both binding affinity and pharmacokinetic properties.

Main Methods:

  • Utilized VolSurf descriptors derived from ligands, proteins, and ligand-protein complexes.
  • Applied these descriptors to model binding for a diverse set of 38 structures (VALIDATE training set).

Related Experiment Videos

  • Applied the same descriptors to a homogeneous set of glycogen phosphorylase inhibitors.

    • Main Results:

    • Achieved a significant model for diverse structures (r(2) = 0.85, q(2) = 0.75).
    • Obtained a highly significant model for glycogen phosphorylase inhibitors (r(2) = 0.94, q(2) = 0.89).
    • Demonstrated the simultaneous modeling of ligand-receptor binding and pharmacokinetic behavior.

    Conclusions:

    • VolSurf descriptors are effective for modeling ligand-receptor binding affinity.
    • The VolSurf computational framework enables simultaneous prediction of binding and pharmacokinetics.
    • This integrated approach supports preclinical drug discovery by evaluating drug candidates more comprehensively.