شماره ركورد كنفرانس :
4719
عنوان مقاله :
QSPR Study of the Complex Formation Constants between β-cyclodextrin and Some Organic Compounds
پديدآورندگان :
Zarei Kobra zarei@du.ac.ir School of chemistry, Damghan Univercity, Damghan, Iran ;E-mail: , Atabati Morteza School of chemistry, Damghan Univercity, Damghan, Iran , Barghebani Efat School of chemistry, Damghan Univercity, Damghan, Iran
كليدواژه :
β , Cyclodextrin , QSPR , Bee Algorithm , ANFIS
عنوان كنفرانس :
بيست و يكمين كنفرانس ملي شيمي فيزيك انجمن شيمي ايران
چكيده فارسي :
Cyclodextrins (CDs) are a group of structurally related natural products and also known as cycloamylosis . Recently CDs have been utilized in many different fields such as catalysis, separation science and technology, drug delivery, pharmaceutical application, food, personal care products and etc[1]. The purpose of this study is to construct a quantitative structure-property relationship (QSPR) model that is able to predict the stability between different guest molecules and β-cyclodextrin. This study is performed using the bee algorithm (BA) and the adaptive neuro-fuzzy inference system (ANFIS). The 3-D structures of 230 compounds [1] were optimized using HyperChem software (version 8.0) with semi empirical AM1 optimization method. After optimization a total of 3224 0-, 1-, 2-, and 3-D descriptors were generated using Dragon software (version 3.0) [2]. In the first, bee algorithm program was written in Matlab in our laboratory by the authors and then was used to select the most important descriptors. Descriptor selection procedure starts with flying of n scout bees toward N-dimensional search space of N descriptors [3].Then the formation constants and error values are calculated using selected descriptors and multiple linear regression model. Finally, the best descriptors are selected due to the less calculated errors. Therefore on the basis of BA, five descriptors were selected and applied as input to the network of the ANFIS. Finally, to evaluate the predictive power of bee-ANFIS the optimized model was applied to all dataset (training, test and validation sets). RMSEs of 0.2995, 0.4213 and 0.3644 were obtained for the training, test and validation sets, respectively. The correlation of coefficient were obtained as 0.9427, 0.8710 and 0.9275 for training, test and validation sets, respectively.