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Evaluating molecular similarity using reduced representations of the electron density
Nathalie Meurice1, Gerald M Maggiora, Daniel P Vercauteren
1Department of Pharmacology and Toxicology, University of Arizona, College of Pharmacy, 1703 E. Mable, Tucson, AZ 85721, USA. meurice@pharmacy.arizona.edu
Journal of Molecular Modeling
|May 13, 2005
Summary
This study introduces GAGS, a genetic algorithm for molecular similarity, to analyze benzodiazepine-like ligands. It identifies key structural features for binding to central and peripheral benzodiazepine receptors.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Pharmacology
Background:
- Benzodiazepine-like ligands interact with central benzodiazepine receptors (CBRs) and peripheral benzodiazepine receptors (PBRs).
- Evaluating molecular similarity is crucial for drug discovery and understanding receptor-ligand interactions.
Purpose of the Study:
- To develop and apply a novel genetic algorithm procedure (GAGS) for assessing molecular similarity of benzodiazepine-like ligands.
- To identify structural features essential for ligand binding to CBRs and PBRs.
Main Methods:
- Utilized a genetic algorithm procedure (GAGS) based on aligning reduced representations of electron density.
- Employed medium crystallographic resolution for electron density computation.
- Compared GAGS alignments with those from MIMIC, a field-based superimposition method.
Main Results:
- GAGS alignments were generally consistent with MIMIC alignments.
- Established relationships between ligand binding affinities and GAGS-derived alignments.
- Determined specific structural features required for significant binding to benzodiazepine receptors.
Conclusions:
- GAGS is an effective method for evaluating molecular similarity and constructing pharmacophore models.
- The study elucidated critical structural determinants for benzodiazepine receptor ligand binding.
- Reduced representations offer benefits for molecular similarity assessments.