The present study deals with the development of an artificial neural network based quantitative structure
activity relationship (QSAR) model for virtual screening of active compounds which contain androstenedione carbonskeleton
or their similar skeleton at the core. An empirical data modeling (with fitted data mapping) has been performed
on the basis of bioassay record for human breast cancer cell line MCF7. The whole experimental data set was considered
as test set. Standard feed-forward back-propagation neural network technique was applied to build the model. Leave-One-
Out (LOO) cross-validation was performed to evaluate the performance of the model. The mapped model became the
basis for selection best mapped compounds followed by development of Pharmacophore specific secondary QSAR model.
In the present study, two best mapped molecules ‘4beta-hydroxy Withanolide-E’ and ‘7, 8-Dehydrocalotropin’ were used
for development of the secondary QSAR model. These secondary-QSAR models were resulted with R2
LOOCV value 0.9845
and 0.9666 respectively. Docking studies, in silico phamacokinetic and toxicity analysis was also done for selected
compounds. The screened compounds CID_73621, CID_16757497, CID_301751, CID_390666 and CID_46830222 were
found with promising binding affinity value with aromatase with reference to the co-crystallized control compound
androstenedione. Due to excellent extent of variance coverage in ANN based QSAR map model, it can be used as a robust
non-linear QSAR model for androstenedione carbon-skeleton containing molecules and the protocol can be used to derive
secondary QSAR models for other compounds set.
Keywords: Androstenedione, ANN, MCF-7, QSAR, withanolide, Virtual Screening, Cytotoxic Activity, model, androstenedione carbonskeleton, non-linear QSAR model.
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