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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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COSMOsar3D: molecular field analysis based on local COSMO σ-profiles.

Andreas Klamt¹, Michael Thormann, Karin Wichmann

¹COSMOlogic GmbH and Co. KG, Burscheider Str. 515, 51381 Leverkusen, Germany. klamt@cosmologic.de

Journal of Chemical Information and Modeling

|July 19, 2012

Summary

The COSMOsar3D method uses local sigma-profiles for 3D-QSAR, offering a novel approach to model ligand-receptor interactions. This method provides robust and predictive models for molecular interactions, outperforming conventional techniques.

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Spatial Separation of Molecular Conformers and Clusters

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Published on: January 9, 2014

Area of Science:

Computational Chemistry
Quantitative Structure-Activity Relationship (QSAR)

Background:

COSMO surface polarization charge density (σ) is a proven descriptor for molecular interactions in liquids.
Previous work introduced grid-based local histograms of σ for 3D molecular similarity and alignment.

Purpose of the Study:

Introduce COSMOsar3D, utilizing local σ-profiles as molecular interaction fields for 3D-QSAR.
Provide a theoretical basis for using these profiles in linear regression, such as PLS.

Main Methods:

Developed COSMOsar3D by employing local σ-profiles derived from quantum chemical calculations and conductor embedding.
Applied these profiles as interaction fields to quantify virtual ligand-receptor interactions, including desolvation.

Main Results:

Demonstrated that logarithmic binding constants are a linear function of local σ-profiles, suitable for PLS analysis.
Showcased COSMOsar3D's ability to generate robust and predictive QSAR models.
Observed that COSMOsar3D models appear superior to those from conventional molecular fields.

Conclusions:

COSMOsar3D offers a novel and effective approach for 3D-QSAR by leveraging local σ-profiles.
The method redefines the ligand-receptor interaction analysis by calculating ligand free energy in a receptor-provided field.
COSMOsar3D shows promise for developing superior predictive models in drug discovery and molecular design.