Local indices for similarity analysis (LISA)-A 3D-QSAR formalism based on local molecular similarity

Verma, Jitender, Malde, Alpeshkumar, Khedkar, Santosh, Iyer, Radhakrishnan and Coutinho, Evans (2009) Local indices for similarity analysis (LISA)-A 3D-QSAR formalism based on local molecular similarity. Journal of Chemical Information and Modeling, 49 12: 2695-2707. doi:10.1021/ci900224u

Author Verma, Jitender
Malde, Alpeshkumar
Khedkar, Santosh
Iyer, Radhakrishnan
Coutinho, Evans
Title Local indices for similarity analysis (LISA)-A 3D-QSAR formalism based on local molecular similarity
Journal name Journal of Chemical Information and Modeling   Check publisher's open access policy
ISSN 1549-9596
Publication date 2009-12
Sub-type Article (original research)
DOI 10.1021/ci900224u
Volume 49
Issue 12
Start page 2695
End page 2707
Total pages 13
Place of publication United States
Publisher American Chemical Society
Language eng
Subject 03 Chemical Sciences
06 Biological Sciences
Abstract A simple quantitative structure activity relationship (QSAR) approach termed local indices for similarity analysis (LISA) has been developed. In this technique, the global molecular similarity is broken up as local similarity at each grid point surrounding the molecules and is used as a QSAR descriptor. In this way, a view of the molecular sites permitting favorable and rational changes to enhance activity is obtained. The local similarity index, calculated on the basis of Petke’s formula, segregates the regions into “equivalent”, “favored similar”, and “disfavored similar” (alternatively “favored dissimilar”) potentials with respect to a reference molecule in the data set. The method has been tested on three large and diverse data sets—thrombin, glycogen phosphorylase b, and thermolysin inhibitors. The QSAR models derived using genetic algorithm incorporated partial least square analysis statistics are found to be comparable to the ones obtained by the standard three-dimensional (3D)-QSAR methods, such as comparative molecular field analysis and comparative molecular similarity indices analysis. The graphical interpretation of the LISA models is straightforward, and the outcome of the models corroborates well with literature data. The LISA models give insight into the binding mechanisms of the ligand with the enzyme and allow fine-tuning of the molecules at the local level to improve their activity.
Q-Index Code C1
Q-Index Status Provisional Code

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Chemistry and Molecular Biosciences
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Created: Wed, 29 Sep 2010, 12:33:24 EST by Laura McTaggart on behalf of Faculty of Science