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Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds
Yanmin Zhang1, Yu Jiao2, Xiao Xiong1
1Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing, 211198, China.
Molecular Diversity
|May 30, 2015
Summary
Discovering novel drug scaffolds is challenging. A new fragment-based virtual screening (VS) approach using Bayesian categorization effectively identifies active scaffolds for targets like VEGFR-2, improving lead compound discovery.
Area of Science:
- Medicinal Chemistry
- Computational Drug Discovery
- Pharmacology
Background:
- Identifying novel scaffolds is crucial but challenging for lead compound discovery.
- Scaffolds binding to active pockets offer greater optimization potential.
- Current databases lack chemical diversity, and screening methods are often ineffective.
Purpose of the Study:
- To propose and validate a fragment-based virtual screening (VS) concept using Bayesian categorization.
- To discover novel active scaffolds against the VEGFR-2 target.
- To evaluate the effectiveness of fragment VS compared to traditional VS.
Main Methods:
- Explicitly evaluated scaffold and structural diversity across 10 compound databases.
- Constructed a Bayesian classification model for screening compound and fragment databases.
- Applied the fragment VS approach to the VEGFR-2 target.
Main Results:
- Bayesian model showed superior performance in screening fragments over whole molecules.
- Scaffold diversity analysis revealed uneven distribution across molecular databases.
- Retrospective literature research confirmed VEGFR-2 biological activity for several high-scoring fragments identified by the model.
Conclusions:
- Fragment-based VS utilizing Bayesian categorization is effective for discovering novel active scaffolds.
- This approach enhances lead discovery by addressing limitations in database diversity and screening efficiency.
- Enriching compound databases with novel structures is essential for improving drug discovery efforts.
