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Computational approaches are widely used almost in all discipline including in drug discovery research. Monte Carlo (MC) algorithm is a randomized algorithm and widely used to solve scientific problems. In the current study, the MC algorithm was used to develop quantitative stricture-activity relationship (QSAR) models from a set of beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) inhibitors to explore important chemical attributes for therapeutic application in Alzheimer’s disease (AD). AD is the neuro-degenerative syndrome and more than 24 millon people affected worldwide. Till date there is no curative drug for AD in the market. It's accepted and proved that the BACE1 is mainly responsible for AD development. Therefore, successful inhibition of BACE1 will be an effective treatment of AD. In this regard, crucial chemical attributes were derived from the BACE1 inhibitors through QSAR modeling to design improved chemical agents for the treatment of AD. The entire collected set of BACE1 inhibitors was divided into training, calibration, test and external sets. Two approaches such, with- and without considering the impact of cyclic rings on inhibitory activity were adopted. QSAR models were generated from the training set and remaining set used to validate the developed models. Both models were found to be statistically robust and consist of mechanistic interpretation. Hence, derived chemical attributes in both models can be used to design and discover improved potential BACE1 inhibitors.

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