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Computational methods for the structural alignment of molecules.

C Lemmen1, T Lengauer

  • 1German National Research Center for Information Technology (GMD), Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany. Christian.Lemmen@combichem.com

Journal of Computer-Aided Molecular Design
|April 11, 2000
PubMed
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This review focuses on automatic molecular superposition methods for drug design when receptor structures are unknown. It highlights techniques for aligning flexible ligands to aid in virtual screening and database searching.

Area of Science:

  • Computational Chemistry
  • Drug Design
  • Structural Bioinformatics

Background:

  • Drug design often lacks receptor structural information, necessitating ligand-based approaches.
  • Numerous ligands with known binding affinities are frequently available.
  • Ligand flexibility poses a significant challenge in accurately determining binding geometry.

Purpose of the Study:

  • To review automatic molecular superposition methods published in the last six years.
  • To focus on methodical aspects of aligning arbitrary molecular structures.
  • To provide insights into computational methods for structural alignment of molecules.

Main Methods:

  • Review of scientific literature on molecular superposition.
  • Focus on automatic procedures for arbitrary molecular structures.

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  • Emphasis on methodical contributions rather than plain application studies.
  • Main Results:

    • Identification of key contributions to computational methods for molecular structural alignment.
    • Highlighting the importance of incorporating molecular flexibility.
    • Discussion of automatic procedures for ligand superposition.

    Conclusions:

    • Automatic molecular superposition is crucial for 3D methods like 3D-QSAR, pharmacophore elucidation, and receptor modeling.
    • These methods are vital for virtual database screening to narrow down experimental search spaces.
    • Future perspectives emphasize the role of superposition in detecting analogues in large structure databases.