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QSAR studies of bioactivities of 1-(azacyclyl)-3-arylsulfonyl-1H-pyrrolo[2,3-b]pyridines as 5-HT6 receptor ligands using physicochemical descriptors and MLR and ANN-modeling.

Abstract
Four molecular descriptors were selected from a pool of variables using genetic algorithm, and then used to built a QSAR model for a series of 1-(azacyclyl)-3-arylsulfonyl-1H-pyrrolo[2,3-b]pyridines as 5-HT(6) receptor agonists or antagonists, useful for the treatment of central nervous system disorders. Simple multiple linear regression (MLR) and a nonlinear method, artificial neural network (ANN), were used to model the bioactivities of the compounds; while MLR gave an acceptable model for predictions, the ANN-based model improved significantly the predictive ability, being more reliable for the prediction and design of novel 5-HT(6) receptor ligands. Topology and molecular/group sizes are important requirements to take into account during the development of novel analogs.
AuthorsMohammad Goodarzi, Matheus P Freitas, Nahid Ghasemi
JournalEuropean journal of medicinal chemistry (Eur J Med Chem) Vol. 45 Issue 9 Pg. 3911-5 (Sep 2010) ISSN: 1768-3254 [Electronic] France
PMID20547432 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
Copyright2010 Elsevier Masson SAS. All rights reserved.
Chemical References
  • Ligands
  • Pyridines
  • Receptors, Serotonin
  • serotonin 6 receptor
Topics
  • Chemical Phenomena
  • Ligands
  • Linear Models
  • Neural Networks, Computer
  • Pyridines (chemistry, metabolism)
  • Quantitative Structure-Activity Relationship
  • Receptors, Serotonin (metabolism)

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