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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Correcting the impact of docking pose generation error on binding affinity prediction.

Hongjian Li¹, Kwong-Sak Leung¹, Man-Hon Wong¹

¹Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.

BMC Bioinformatics

|February 11, 2017

Summary

Pose generation error in drug discovery has minimal impact on binding affinity prediction accuracy. A new calibration method using re-docked poses significantly improves prediction by correcting for this error.

Keywords:

Binding affinity Drug discovery Machine learning Molecular docking

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Area of Science:

Computational chemistry
Drug discovery
Molecular modeling

Background:

Pose generation error, the discrepancy between predicted and actual molecular geometry, is a common issue in docking.
The impact of this error on binding affinity prediction accuracy remains under-investigated across various protein-ligand systems.

Purpose of the Study:

To systematically analyze the effect of pose generation error on binding affinity prediction.
To develop and validate a method for correcting pose generation errors in binding affinity prediction.

Main Methods:

Evaluation of pose generation error impact on binding affinity prediction using both machine-learning and classical scoring functions (e.g., AutoDock Vina).
Development of a calibration procedure using re-docked poses instead of co-crystallized poses to correct for pose generation errors.
Comparison of prediction accuracy using single versus multiple docked poses per ligand.

Main Results:

Pose generation error generally has a minor impact on binding affinity prediction accuracy, even for large errors.
The proposed calibration method significantly improves prediction accuracy, bringing it closer to predictions made without pose generation error.
Strategies using a single docked pose per ligand outperformed those using multiple poses.

Conclusions:

Pose generation error is less detrimental to binding affinity prediction than commonly assumed.
A novel calibration procedure effectively corrects for pose generation errors, enhancing prediction reliability.
The developed machine-learning scoring function is available for use in drug discovery research.