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Identification of common functional configurations among molecules
D Barnum1, J Greene, A Smellie
1Molecular Simulations Incorporated, Sunnyvale, California 94086, USA.
Journal of Chemical Information and Computer Sciences
|May 1, 1996
Summary
A novel algorithm identifies common 3D chemical structures in molecules, prioritizing those that are both prevalent and rare. This method aids in discovering potent drug candidates, like novel HIV reverse transcriptase inhibitors.
Area of Science:
- Computational chemistry
- Drug discovery
- Structural biology
Background:
- Identifying common three-dimensional (3D) molecular configurations is crucial for understanding structure-activity relationships.
- Existing methods may struggle with large datasets of molecular conformations.
Purpose of the Study:
- To introduce and evaluate a new algorithm for identifying common 3D chemical feature configurations across molecular sets.
- To assess the algorithm's performance on diverse pharmaceutical datasets.
Main Methods:
- Developed a novel algorithm that scores molecular configurations based on prevalence and estimated rarity.
- Applied the algorithm to datasets including PAF antagonists, HIV reverse transcriptase inhibitors, and HIV protease inhibitors.
- The algorithm accommodates molecules with hundreds of conformational models.
Main Results:
- Successfully applied the algorithm to three distinct chemical datasets.
- Identified a significant common configuration among potent HIV reverse transcriptase inhibitors.
- This identified configuration is shared by recently reported, highly effective inhibitors.
Conclusions:
- The new algorithm effectively identifies biologically relevant 3D molecular configurations.
- It offers a valuable tool for drug discovery, particularly for complex molecular systems.
- The findings highlight a shared structural motif in potent HIV reverse transcriptase inhibitors.