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Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges
Bentley M Wingert1, Rick Oerlemans2, Carlos J Camacho3
1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
Virtual screening methods for drug discovery were evaluated. Docking to a single optimal receptor improved binding affinity rankings for open targets, outperforming complex methods.
Area of Science:
- Computational chemistry
- Drug discovery
- Molecular modeling
Background:
- Virtual screening aims to identify potent drug candidates from vast chemical libraries.
- Accurate ranking of compound binding affinity is crucial for effective virtual screening.
- Previous evaluations highlighted the need for optimized docking strategies.
Purpose of the Study:
- To evaluate and compare different virtual screening and compound ranking strategies.
- To identify optimal docking approaches for various target pocket types (deep vs. open).
- To assess the impact of receptor flexibility and selection on affinity prediction accuracy.
Main Methods:
- Prospective evaluation of docking and free energy calculation methods in D3R grand challenges.
- Docking compounds to holo-receptors with chemically similar ligands for deep pockets.
- Docking to single optimal receptors for open pockets, avoiding multiple receptor structures.
- Analysis of Spearman correlation coefficients to assess ranking performance.
- Evaluation of free energy calculations for congeneric compounds.
Main Results:
- For deep-pocket targets, docking to holo-receptors with similar ligands yielded the best correlations (Spearman ρ ≈ 0.5).
- For open-pocket targets, docking to a single optimal receptor outperformed using multiple structures (Spearman ρ ≈ 0.5).
- Suboptimal receptor selection for cross-docking significantly degraded affinity rankings.
- Free energy calculations for congeneric compounds showed competitive performance.
- Automated virtual screening with rigid receptors demonstrated superior performance over flexible docking.
Conclusions:
- The choice of docking strategy significantly impacts the success of virtual screening.
- Single optimal receptor docking is effective for open targets, while holo-receptor docking suits deep targets.
- Rigid receptor docking in automated virtual screening offers a robust and efficient approach for affinity prediction.
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