Background: Depression is a mental illness caused by the imbalance of important neurotransmitters such as serotonin (5-HT) and norepinephrine (NE). It is a serious neurological disorder that could be treated by antidepressant drugs.
Objective: There are two major classes such as TCAs and phenoxyphenylpropylamines which have been proven to be
broad-spectrum antidepressant compounds. Several attempts were made to design, synthesize and discover potent antidepressant compounds having the least toxicity and most selectivity towards serotonin and norepinephrine transporters. But
there is hardly any drug design based on quantitative structure-activity relationship (QSAR) and pharmacophore modeling
Method: In the present study, many TCAs (dibenzoazepine) and phenoxyphenylpropylamine derivatives are taken into consideration for pharmacophore feature generation followed by pharmacophoric distant related descriptors based QSAR modeling. Further, several five new congeners have been designed which are subjected to the prediction of biological activities
in terms of serotonin receptor affinity utilizing validated QSAR models developed by us.
Results: An important pharmacophoric feature point C followed by the generation of a topography of the TCAs and phenoxyphenylpropylamine has been predicted. The developed pharmacophoric feature-based QSAR can explain 64.2% of the
variances of 5-HT receptor antagonism. The best training model has been statistically validated by the prediction of test set
compounds. This training model has been used for the prediction of some newly designed congeneric compounds which are
comparable with the existed drugs.
Conclusion: The newly designed compounds may be proposed for further synthesis and biological screening as antidepressant agents.