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Using buriedness to improve discrimination between actives and inactives in docking
Noel M O'Boyle1, Suzanne C Brewerton, Robin Taylor
1Cambridge Crystallographic Data Centre, Cambridge, UK. oboyle@ccdc.cam.ac.uk
Journal of Chemical Information and Modeling
|June 7, 2008
Summary
Improving protein-ligand docking accuracy, this study enhances the ChemScore function by scaling interactions based on their buriedness. This method significantly improves the ranking of active molecules over inactives in drug discovery.
Area of Science:
- Computational chemistry
- Structural biology
- Drug discovery
Background:
- Accurate ranking of active vs. inactive molecules in protein-ligand docking remains a challenge.
- Existing scoring functions may not adequately account for the spatial context of interactions.
Purpose of the Study:
- To improve the ChemScore scoring function within the GOLD docking software.
- To investigate the impact of interaction buriedness on scoring accuracy.
Main Methods:
- Developed a method to scale hydrogen bond, metal-ligand, and lipophilic interactions based on 'buriedness'.
- Measured buriedness using receptor density (protein heavy atoms within 8.0 Å).
- Optimized scaling parameters using docked poses of inactive molecules (negative data).
Main Results:
- A four-parameter model incorporating buriedness scaling improved the average rank of active molecules from 18.6 to 12.5.
- Results were validated on an independent test set, showing similar improvements.
- Receptor density scaling is now an option in the GOLD software.
Conclusions:
- Scaling interactions by buriedness is an effective strategy to enhance protein-ligand docking accuracy.
- This refinement leads to better discrimination between active and inactive compounds.
- The implemented method offers a practical improvement for drug discovery pipelines.
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