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.
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.
Goodarzi
Mohammad
M
Department of Chemistry, Faculty of Sciences, Islamic Azad University, Arak Branch, Arak, Markazi, Iran.
Freitas
Matheus P
MP
Ghasemi
Nahid
N
D055598:Chemical Phenomena; D008024:Ligands; D016014:Linear Models; D016571:Neural Networks, Computer; D011725:Pyridines / Q000737:chemistry* / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship; D011985:Receptors, Serotonin / Q000378:metabolism*
D008024:Ligands; D011725:Pyridines; D011985:Receptors, Serotonin; C097216:serotonin 6 receptor