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Strategies for indirect computer-aided drug design

G H Loew1, H O Villar, I Alkorta

  • 1Molecular Research Institute, Palo Alto, California 94304.

Pharmaceutical Research
|April 1, 1993
PubMed
Summary

Computer-aided drug design for CNS targets without known structures uses indirect methods like 2D-QSAR and pharmacophore development. These strategies characterize ligands to inform drug discovery when direct target interaction is unknown.

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Pharmacology

Background:

  • Drug design often relies on known 3D structures of macromolecular targets for direct ligand-receptor interaction analysis.
  • However, for many drugs, particularly those acting on the central nervous system (CNS), target structures are unknown, necessitating indirect design strategies.

Purpose of the Study:

  • This review details indirect computer-aided drug design (CADD) methods for targets with unknown structures.
  • It focuses on ligand characterization techniques and emerging approaches to bridge the gap towards direct design.

Main Methods:

  • The review covers Quantitative Structure-Activity Relationship (QSAR) methods, specifically 2D-QSAR.
  • Pharmacophore development at various levels, detailing molecular properties influencing receptor recognition, is extensively discussed.
  • Methods for generating model receptor structures are also briefly presented.

Main Results:

  • Indirect methods like 2D-QSAR and pharmacophore modeling provide crucial ligand property information for drug design.
  • These approaches enable the characterization of ligands when direct target structural information is unavailable.
  • Emerging techniques aim to create model receptor structures, facilitating a transition towards direct CADD.

Conclusions:

  • Indirect CADD methods are essential for designing drugs targeting macromolecules with unknown structures, such as many CNS-active agents.
  • Pharmacophore development offers a versatile approach with varying levels of detail regarding molecular properties.
  • Future developments in computational chemistry will enhance the effectiveness of CADD for novel drug discovery.

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