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CoRILISA: a local similarity based receptor dependent QSAR method.

Vijay M Khedkar1, Evans C Coutinho

  • 1Department of Pharmaceutical Chemistry, Bombay College of Pharmacy , Kalina, Santacruz (E), Mumbai 400098, India.

Journal of Chemical Information and Modeling
|December 24, 2014
PubMed
Summary
This summary is machine-generated.

A new computational method, CoRILISA, enhances drug design by analyzing local molecular similarities and receptor attributes. This approach offers superior prediction accuracy for drug activity compared to existing methods.

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Drug discovery

Background:

  • Structure-based drug design relies on molecular similarity but current methods offer limited local spatial information.
  • Existing approaches provide a global view, hindering detailed analysis of activity variations linked to 3D structure.

Purpose of the Study:

  • To introduce CoRILISA, a novel receptor-dependent quantitative structure-activity relationship (QSAR) method.
  • To improve molecular similarity analysis by incorporating receptor attributes for enhanced drug design insights.

Main Methods:

  • CoRILISA integrates previously developed CoRIA and LISA methodologies.
  • It calculates and compares molecular similarity using both ligand and receptor structural features.
  • Validated on glycogen phosphorylase b, HIV-1 protease, and CDK2 inhibitor datasets.

Main Results:

  • CoRILISA models demonstrated significantly improved predictivity, especially for test sets, compared to the standard CoRIA approach.
  • The method successfully identified thermodynamic properties of active site residues influencing molecular activity.
  • Outperformed other published methods in benchmark studies.

Conclusions:

  • CoRILISA provides a more detailed understanding of ligand-receptor interactions than global similarity measures.
  • It is a valuable tool for fragment-based drug discovery, aiding in ligand activity prediction.
  • The method enhances rational drug design by linking activity variations to specific receptor and ligand structural features.