Quantitative structure-activity relationship of some flavonoid derivatives as acetylcholinesterase inhibitors based on Monte Carlo algorithm
Subject Areas :Zahra Nazari 1 , Shahin Ahmadi 2 * , Ali Almasirad 3
1 - Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
2 - Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
3 - Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
Keywords: QSAR, Flavonoids, Acetylcholinesterase inhibitor, SMILES, Index of ideality of correlation.,
Abstract :
A method based on an independent approach of molecule optimization based on structural and topological molecular features has recently been introduced as an attractive approach in the QSAR modeling. A key feature of this approach refers to the use of descriptors based on the Simplified Molecular Input Line Input System (SMILES) to develop the QSAR model. In this research, by studying the QSAR of some flavonoid derivatives as acetylcholinesterase inhibitors based on the Monte Carlo algorithm, a set of 88 acetylcholinesterase inhibitors was selected from peer reviewed papers. The structures of the compounds in SMILES format were defined as software input files and randomly divided into four series including training, invisible training, calibration and validation sets and modeling was done. The optimal descriptors used in the model were selected from the combination of SMILES and hydrogen suppressor graph based on a new objective function. Finally, the predictive power of models was evaluated by validation sets. The results of four random series showed that predictive and statistically reliable models were obtained. The most important descriptors that increase and decrease the acetylcholinesterase inhibitory activity of flavonoid derivatives were identified. Finally, acetylcholinesterase inhibitory (pIC50) of a large number of natural flavonoid derivatives were calculated and reported. Using the obtained model, it is possible to predict pIC50 of flavonoid derivatives as acetylcholinesterase enzyme inhibitors using Monte Carlo and SMILES optimization methods
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