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.
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Authors | Mohammad Goodarzi, Matheus P Freitas, Nahid Ghasemi |
Journal | European journal of medicinal chemistry
(Eur J Med Chem)
Vol. 45
Issue 9
Pg. 3911-5
(Sep 2010)
ISSN: 1768-3254 [Electronic] France |
PMID | 20547432
(Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
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Copyright | 2010 Elsevier Masson SAS. All rights reserved. |
Chemical References |
- Ligands
- Pyridines
- Receptors, Serotonin
- serotonin 6 receptor
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Topics |
- Chemical Phenomena
- Ligands
- Linear Models
- Neural Networks, Computer
- Pyridines
(chemistry, metabolism)
- Quantitative Structure-Activity Relationship
- Receptors, Serotonin
(metabolism)
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