Literature DB >> 22804925

COSMOsim3D: 3D-similarity and alignment based on COSMO polarization charge densities.

Michael Thormann1, Andreas Klamt, Karin Wichmann.   

Abstract

COSMO σ-surfaces resulting from quantum chemical calculations of molecules in a simulated conductor, and their histograms, the so-called σ-profiles, are widely proven to provide a very suitable and almost complete basis for the description of molecular interactions in condensed systems. The COSMOsim method therefore introduced a global measure of molecular similarity on the basis of similarity of σ-profiles, but it had the disadvantage of neglecting the 3D distribution of molecular polarities, which is crucially determining all ligand-receptor binding. This disadvantage is now overcome by COSMOsim3D, which is a logical and physically sound extension of the COSMOsim method, which uses local σ-profiles on a spatial grid. This new method is used to measure intermolecular similarities on the basis of the 3D representation of the surface polarization charge densities σ of the target and the probe molecule. The probe molecule is translated and rotated in space in order to maximize the sum of local σ-profile similarities between target and probe. This sum, the COSMOsim3D similarity, is a powerful descriptor of ligand similarity and allows for a good discrimination between bioisosters and random pairs. Validation experiments using about 600 pharmacological activity classes in the MDDR database are given. Furthermore, COSMOsim3D represents a unique and very robust method for a field-based ligand-ligand alignment.

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Year:  2012        PMID: 22804925     DOI: 10.1021/ci300205p

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  3 in total

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Authors:  Benjamin P Brown; Jeffrey Mendenhall; Jens Meiler
Journal:  J Chem Inf Model       Date:  2019-02-12       Impact factor: 4.956

2.  Application of the quantum mechanical IEF/PCM-MST hydrophobic descriptors to selectivity in ligand binding.

Authors:  Tiziana Ginex; Jordi Muñoz-Muriedas; Enric Herrero; Enric Gibert; Pietro Cozzini; F Javier Luque
Journal:  J Mol Model       Date:  2016-05-17       Impact factor: 1.810

3.  Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

Authors:  Taeho Kim; Byoung Hoon You; Songhee Han; Ho Chul Shin; Kee-Choo Chung; Hwangseo Park
Journal:  Int J Mol Sci       Date:  2021-10-12       Impact factor: 5.923

  3 in total

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