|تعداد مشاهده مقاله||22,880,891|
|تعداد دریافت فایل اصل مقاله||21,118,438|
Quantitative structure–property relationship models to Predict some thermodynamic properties of Imidazole Derivatives using molecular descriptor and genetic algorithm-multiple linear regressions
|Journal of Physical & Theoretical Chemistry|
|دوره 18، شماره 2 - شماره پیاپی 3، بهمن 2021، صفحه 23-44 اصل مقاله (786.48 K)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.30495/jptc.2021.20401|
|shiva Moshayedi1؛ fatemeh shafiei 2؛ Tahereh Momeni Isfahani 3|
|1Tarbiat Modares University, Department of Materials, Tehran, Iran|
|2Department of Chemistry, Science Faculty, Arak Branch, Islamic Azad University, Arak, Iran|
|3Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran|
|Imidazole is compound with a wide range of biological activities and imidazole derivatives are the basis of several groups of drugs.|
In this study the relationship between molecular descriptors and the thermal energy (Eth kJ/mol), and heat capacity (Cv J/mol) of imidazole derivatives is studied.
The chemical structures of 85 Imidazole derivatives were optimized at HF/6-311G* level with Gaussian 98 software.
Molecular descriptors were calculated for selected compound by using the Dragon software.
The Genetic algorithm- multiple linear regression (GA-MLR) and backward methods were used to select the suitable descriptors and also for predicting the thermodynamic properties of imidazole derivatives.
The obtained models were evaluated by statistical parameters, such as correlation coefficient (R2adj), Fisher ratio (F), Root Mean Square Error (RMSE), Durbin-Watson statistic (D) and significance (Sig).
The predictive powers of the GA- MLR models are studied using leave-one-out (LOO) cross-validation and external test set.
The predictive ability of the GA-MLR models with two-three selected molecular descriptors was found to be satisfactory. The developed QSPR models can be used to predict the property of compounds not yet synthesized.
|QSPR؛ imidazole derivatives؛ leave-one-out (LOO) cross-validation؛ genetic algorithm- multiple linear regressions|
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