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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

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QM/QM docking method based on the variational finite localized molecular orbital approximation.

Victor M Anisimov¹, Vladislav L Bugaenko

¹FQS Poland Ltd., Starowislna 13, Krakow 31-038, Poland. v.anisimov@fqs.pl

Journal of Computational Chemistry

|August 30, 2008

Summary

We developed a new quantum mechanical QM/QM method for drug discovery. This computational approach efficiently predicts potent inhibitors for protein targets like p56 LCK SH2, accelerating drug development.

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Area of Science:

Computational Chemistry
Drug Discovery
Molecular Modeling

Background:

The variational finite localized molecular orbital (VFL) approximation provides a framework for accurate molecular calculations.
Efficient computational methods are crucial for large-scale virtual screening in drug discovery.

Purpose of the Study:

To introduce and validate a novel semiempirical quantum mechanical QM/QM method based on the VFL approximation.
To apply the developed QM/QM method for identifying potent inhibitors using QM docking against the p56 LCK SH2 domain.

Main Methods:

Derivation of the semiempirical variational finite localized molecular orbital (VFL) approximation.
Development of a novel QM/QM method treating the active site self-consistently and the protein bulk with a frozen density matrix.
Application to QM docking studies on the p56 LCK SH2 domain, screening 20,000 drug-like molecules.

Main Results:

The QM/QM method enabled virtual screening of 200,000 poses, identifying 10 potent inhibitors.
Energy calculations for complexes (approx. 1700 atoms) took 14.54 s/CPU at the NDDO AM1 level.
Flexible ligand docking studies required 153.03 s/CPU per complex, with cluster computations finishing in half a day.

Conclusions:

The developed QM/QM method is computationally efficient for large-scale QM docking.
This approach significantly accelerates the identification of potential drug candidates for specific protein targets.