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Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
Published on: May 16, 2021
Ligand efficiency and fragment-based drug discovery.
Scott D Bembenek1, Brett A Tounge, Charles H Reynolds
1Johnson & Johnson Pharmaceutical Research and Development, LLC, San Diego, CA 92121, United States. sbembene@its.jnj.com
Drug Discovery Today
|December 17, 2008
Summary
Fragment-based drug discovery (FBDD) yields optimized compounds. A new size-independent score, fit quality, improves upon traditional ligand efficiency (LE) for assessing drug candidates.
Area of Science:
- Drug discovery and medicinal chemistry
- Computational chemistry
- Biochemistry
Background:
- Fragment-based drug discovery (FBDD) is increasingly utilized for developing optimized, low molecular weight compounds.
- Ligand efficiency (LE) is a key metric for evaluating fragments and drug candidates.
- LE is size-dependent, limiting its utility for comparing molecules of different molecular weights.
Purpose of the Study:
- To introduce a novel, size-independent efficiency score called fit quality.
- To assess the effectiveness of LE and fit quality in evaluating FBDD optimization.
- To compare the performance of LE and fit quality using literature-derived FBDD examples.
Main Methods:
- Literature review of fragment-based drug discovery (FBDD) case studies.
- Calculation and comparison of Ligand Efficiency (LE) for various molecular structures.
- Calculation and comparison of the novel fit quality score for assessing ligand optimization.
Main Results:
- Starting fragments in FBDD generally exhibit higher LE than larger, optimized structures.
- The novel fit quality score demonstrates improvement upon fragment optimization.
- Fit quality offers a size-independent assessment of ligand efficiency.
Conclusions:
- Fit quality provides a more reliable measure of ligand optimization in FBDD compared to LE.
- The developed fit quality score addresses the limitations of LE in assessing compounds of differing molecular weights.
- This metric aids in the rational design and optimization of drug candidates within FBDD workflows.
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