QSAR and docking studies on the (5-nitroheteroaryl-1,3,4-thiadiazole-2-yl) piperazinyl analogs with antileishmanial activity

QSAR and docking studies on the (5-nitroheteroaryl-1,3,4-thiadiazole-2-yl) piperazinyl analogs with antileishmanial activity


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دانشگاه علوم پزشکی تبریز
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نویسندگان: سیاوش دستمالچی , مریم حمزه میوه رود

کلمات کلیدی: leishmaniasis; QSAR; ANN; MLR; molecular modeling

نشریه: , 30 , 5 , 2016

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نویسنده ثبت کننده مقاله سیاوش دستمالچی
مرحله جاری مقاله تایید نهایی
دانشکده/مرکز مربوطه مرکز تحقیقات بیوتکنولوژی(زیست فناوری)
کد مقاله 59343
عنوان فارسی مقاله QSAR and docking studies on the (5-nitroheteroaryl-1,3,4-thiadiazole-2-yl) piperazinyl analogs with antileishmanial activity
عنوان لاتین مقاله QSAR and docking studies on the (5-nitroheteroaryl-1,3,4-thiadiazole-2-yl) piperazinyl analogs with antileishmanial activity
ناشر 5
آیا مقاله از طرح تحقیقاتی و یا منتورشیپ استخراج شده است؟ بلی
عنوان نشریه (خارج از لیست فوق) Journal of Chemometrics
نوع مقاله Original Article
نحوه ایندکس شدن مقاله ایندکس شده سطح یک – ISI - Web of Science
آدرس لینک مقاله/ همایش در شبکه اینترنت http://onlinelibrary.wiley.com/doi/10.1002/cem.2789/

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Leishmaniasis is a disease caused by a protozoan parasites belonging to the genus Leishmania. It causes morbidity and mortality in the tropical and subtropical regions. Current drugs are toxic, expensive, and require long-term treatment. Thus, identification and development of novel, cheap, efficient, and safe antileishmanial compounds as drug candidates are important from pharmaceutical point of view. Quantitative structure–activity relationship (QSAR) methods are used to predict the pharmaceutically relevant properties of drug candidates whenever it is applicable. The aim of this study was to use two different techniques, namely multiple linear regression (MLR) and artificial neural networks (ANNs) in predicting the antileishmanial activity (i.e. pIC50) of 5-(5-nitroheteroaryl-2- y1)-1,3,4-thiadiazole derivatives. To this end, genetic algorithm-coupled partial least square and backward multiple regression method were used to select a number of calculated molecular descriptors to be used in MLR and ANNbased QSAR studies. The predictive power of the models was also assessed using leave-one-out and leave-groupout cross validation methods. Also, molecular modeling studies were conducted based on DNA topoisomerase I to identify the binding interactions responsible for antileishmanial activity of those analogs. The results suggest that hydrogen bonding interactions and several hydrophobic interactions of ligands with the active site of Leishmania major topoisomerase IB are responsible for their potent antileishmanial activity. These results can be exploited for structure-based computer-aided drug designing of new and selective leishmania topoisomerase inhibitors.

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سیاوش دستمالچیپنجم
مریم حمزه میوه روددوم

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