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Improving performance of docking-based virtual screening by structural filtration.

Fedor N Novikov¹, Viktor S Stroylov, Oleg V Stroganov

¹MolTech Ltd, 119992, Moscow, Leninskie gory, 1/75A, Russia.

Journal of Molecular Modeling

|December 31, 2009

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This study introduces structural filtration to enhance virtual screening accuracy. This method improves drug discovery by reducing false positives and negatives in molecular docking.

Area of Science:

Computational chemistry
Structural biology
Drug discovery

Background:

Virtual screening methods often suffer from scoring function deficiencies.
These deficiencies can lead to overestimation of decoy binding and a high false positive rate.
Protein structure model inaccuracies can also result in false negative predictions.

Purpose of the Study:

To introduce and evaluate an innovative structural filtration method for improving virtual screening results.
To address limitations in current virtual screening scoring functions and protein structure models.
To enhance the accuracy and efficiency of identifying potential drug candidates.

Main Methods:

Developed protein-specific structural filters based on conserved, crucial protein-ligand interactions.

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Applied these filters to virtual screening results obtained using Lead Finder software.

Evaluated the method on 10 diverse protein targets.

Main Results:

Structural filtration significantly improved the enrichment factor, ranging from several-fold to hundreds-fold increases.
The method effectively corrected scoring function overestimations of decoy binding, lowering the false positive rate.
Structural filters also mitigated false negatives arising from protein structure model deficiencies.

Conclusions:

Structural filtration is an effective technique for enhancing virtual screening accuracy.
This approach shows promise for fragment-based drug discovery by identifying specifically bound small molecules.
The method offers a valuable tool for improving the reliability of computational drug discovery pipelines.