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The Design of Leadlike Combinatorial Libraries
1Department of Medicinal Chemistry, AstraZeneca R&D Charnwood, Bakewell Road, Loughborough, Leicestershire LE11 5RH (UK).
Angewandte Chemie (International Ed. in English)
|January 29, 2000
Summary
Optimizing low-potency drug leads often increases molecular weight and lipophilicity. Screening leadlike libraries provides room for this optimization, unlike typical combinatorial libraries.
Area of Science:
- Medicinal Chemistry
- Drug Discovery
Background:
- Drug development often involves optimizing low-potency compounds.
- This optimization frequently leads to increased molecular weight and lipophilicity.
Purpose of the Study:
- To analyze the impact of affinity enhancement on molecular properties during drug lead optimization.
- To compare molecular weight distributions across different compound libraries and oral drugs.
Main Methods:
- Analysis of molecular weight (M(r)) distributions.
- Comparison of M(r) data for leadlike libraries, oral drugs, and combinatorial chemistry libraries.
Main Results:
- Optimization of low-potency leads typically increases M(r) and lipophilicity.
- Hits from leadlike libraries (µM affinity) offer greater potential for optimization compared to typical combinatorial libraries.
- Oral drugs exhibit a distinct M(r) distribution compared to leadlike and combinatorial libraries.
Conclusions:
- Leadlike libraries are suitable for discovering compounds that can be optimized into drug candidates.
- The process of drug optimization inherently involves trade-offs in molecular properties like M(r) and lipophilicity.