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حرية الوصول المقاله
1 - QSAR study of camptothecin derivatives as anticancer drugs using genetic algorithm and multiple linear regression analysis
fatemeh shafiei Shahaboddin Mohebbi Tahereh Momeni Isfahani Mehdi Ahmadi SabeghA quantitative structure- activity relationship (QSAR) has been widely used to investigation a correlation between chemical structures of molecules to their activities. In the present study, QSAR models have been carried out on 76 camptothecin (CPT) derivatives as antic أکثرA quantitative structure- activity relationship (QSAR) has been widely used to investigation a correlation between chemical structures of molecules to their activities. In the present study, QSAR models have been carried out on 76 camptothecin (CPT) derivatives as anticancer drugs to determine the 14N nucleus quadrupole coupling constants (QCC). These quantum chemical properties have been calculated using Density Functional Theory (DFT) and B3LYP/6-311G (d, p) method in the gas phase. A training set of 60 CPT derivatives were used to construct QSAR models and a test set of 16 compounds were used to evaluate the build models that were made using multiple linear regression (MLR) analysis. Molecular descriptors were calculated by Dragon software, and the stepwise multiple linear regression and the Genetic algorithm (GA) techniques were used to select the best descriptors and build QSAR models respectively. QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and external validation methods. The multicollinearity of the descriptors contributed in the models was tested by calculating the variance inflation factor (VIF) and the DurbinWatson (DW) statistics. The predictive ability of the models was found to be satisfactory. The results of QSAR study show that quantum parameters, 2D autocorrelations and Walk and path counts descriptors contains important structural information sufficient to develop useful predictive models for the studied activities. تفاصيل المقالة -
حرية الوصول المقاله
2 - QSPR Models to Predict Thermodynamic Properties of Alkenes Using Genetic Algorithm and Backward- Multiple Linear Regressions Methods
fatemeh Ghaemdoost fatemeh shafieiQuantitative structureproperty relationship (QSPR) models establish relationships between different types of structural information to their properties. In the present study the relationship between the molecular descriptors and quantum properties consist of the heat ca أکثرQuantitative structureproperty relationship (QSPR) models establish relationships between different types of structural information to their properties. In the present study the relationship between the molecular descriptors and quantum properties consist of the heat capacity (Cv/J mol-1K-1) entropy (S/J mol-1K-1) and thermal energy (Eth/kJ mol-1) of 100 alkenes is represented. Genetic algorithm (GA) and backward-multiple linear regressions (BW-MLR) were successfully developed to predict quantum properties of alkenes. Molecular descriptors were calculated with Dragon software and the genetic algorithm (GA) method was used to selected important molecular descriptors. The quantum properties were obtained from quantum-chemistry technique at the Hartree-Fock (HF) level using the ab initio 6-31G* basis sets. The predictive powers of the BW-MLR models were discussed by using leave-one-out (LOO) cross-validation and external test set. Results showed that the predictive ability of the models was satisfactory, and the 2D matrix-based descriptors, topological, edge adjacency and Connectivity indices could be used to predict the mentioned properties of 100 alkenes تفاصيل المقالة -
حرية الوصول المقاله
3 - Theoretical studies on corrosion inhibition of N-aroyl-N’-aryl thiourea derivatives using conceptual DFT approach
Semire Banjo Oyebamiji Abel KolawoleIn this paper, quantum chemical parameters at density functional theory (DFT) B3LYP/6-31G** (d,p) level of theory were calculated for three organic corrosion inhibitors [N-benzoyl-N-(p-aminophenyl) thiourea, N-benzoyl-N-(thiazole) thiourea and N-acetyl-N-(dibenzyl) thio أکثرIn this paper, quantum chemical parameters at density functional theory (DFT) B3LYP/6-31G** (d,p) level of theory were calculated for three organic corrosion inhibitors [N-benzoyl-N-(p-aminophenyl) thiourea, N-benzoyl-N-(thiazole) thiourea and N-acetyl-N-(dibenzyl) thiourea. The calculated molecular descriptors such as the HOMO, LUMO, dipole moment, chemical potential (μ), chemical hardness (ղ), global nucleophilicity (ω) and average electronic charges on nitrogen atoms were explained in line with the experimental observed inhibitory efficiency for the compounds. The calculated results revealed that electron density on the rings (Qring (e)) and thiocarbonyl sulphur atom (S* in C=S) are strongly correlated to the observed %IE. Therefore, electronic interactions such as π-cationic and n-cationic interactions between the molecules and metal surface played prominent roles in adsorption process than electron donor-acceptor model as early reported by Uday et al., 2013 [19]. تفاصيل المقالة -
حرية الوصول المقاله
4 - مراحل، محاسبات و نتایج حاصل از مطالعات پیش‌بینی‌های نظری ارتباط کمی ساختار بازداری (QSRR) اسانس گیاه میخک زینتی
مجید محمدحسینی مهدی نکوئیدر این مقاله، به تشریح مبسوط مدل‌های خطی توانمند در پیش بینی شاخص بازداری کواتس گروه وسیعی از ترکیبات طبیعی شناسایی شده در روغن اسانسی گیاه میخک زینتی به عنوان یکی از گیاهان دارویی پرداخته شده است. در این راستا، اساس کار مبتنی بر روابط کمی ساختار بازداری (QSRR) می‌باشد أکثردر این مقاله، به تشریح مبسوط مدل‌های خطی توانمند در پیش بینی شاخص بازداری کواتس گروه وسیعی از ترکیبات طبیعی شناسایی شده در روغن اسانسی گیاه میخک زینتی به عنوان یکی از گیاهان دارویی پرداخته شده است. در این راستا، اساس کار مبتنی بر روابط کمی ساختار بازداری (QSRR) می‌باشد که در منابع علمی از اهمیت بسزایی جهت برقراری ارتباط منطقی و هدفمند بین شاخص کواتس به عنوان یک متغیر وابسته و گروهی از توصیف‌کننده‌های مولکولی به عنوان متغیر‌های مستقل برخوردار است. در این راستا، پس از ترسیم ساختار ترکیبات مفروض در محیط نرم‌افزار هایپرکم و بهینه‌سازی آن‌ها، جهت استخراج توصیف‌کننده‌های مولکولی مربوطه از نرم‌افزار دراگون استفاده شد. در مرحله بعد، پس از حذف توصیف‌کننده‌های غیر مرتبط و اضافی، نهایتاً با روش‌های مرحله‌ای و روش انتخاب متغیر مبتنی بر الگوریتم ژنتیک گروهی از توصیف‌کننده‌های مهم و مؤثر شناسایی و ارتباط خطی آن‌ها با شاخص بازداری کواتس مورد بحث و بررسی قرار گرفت. نتایج حاصله حاکی از توانمندی بالای مدل‌های ارائه شده جهت پیش بینی شاخص کواتس گروه وسیعی از ترکیبات طبیعی دارد. تفاصيل المقالة -
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5 - A Robust Quantitative Structure-Property Relationship-Based Model for Estimation of Refractivity Indices of 101 Common Paraffin Derivatives Based on their Molecular Structures
Mehdi NekoeiA robust linear quantitative structure-property relationship (QSPR) model has been constructed to model and predict the refractivity indices of 101 organic compounds as common halo-derivatives of normal paraffin by application of the structural descriptors combined with أکثرA robust linear quantitative structure-property relationship (QSPR) model has been constructed to model and predict the refractivity indices of 101 organic compounds as common halo-derivatives of normal paraffin by application of the structural descriptors combined with multiple linear regressions (MLR) method. In the main part of this study, theoretical molecular descriptors were adopted from the original pool through the stepwise feature selection method. A simple model with low standard errors and promising correlation coefficients was obtained. MLR method could model the relationship between refractivity and structural descriptors, perfectly. The accuracy of the proposed MLR model was illustrated using cross-validation, validation through an external test set, and Y-randomization techniques. The linear techniques such as MLR combined with a successful variable selection procedure are capable of generating an efficient QSPR model for predicting the refractivity indices of different compounds. The constructed model, with high statistical significance (R2train = 0.926; Ftrain = 240.675; R2test = 0.947; Ftest = 52.978; REP (%) = 1.219; Q2LOO = 0.914 and Q2LGO = 0.914), could be adequately used for the prediction and description of the affecting parameters on refractivity behavior of similar or even unknown compounds. تفاصيل المقالة -
حرية الوصول المقاله
6 - Novel QSPR Study on the Melting Points of a Broad Set of Drug-Like Compounds Using the Genetic Algorithm Feature Selection Approach Combined With Multiple Linear Regression and Support Vector Machine
Alireza Jalali Mehdi Nekoei Majid MohammadhosseiniA robust and reliable quantitative structure-property relationship (QSPR) study was established to forecast the melting points (MPs)Â of a diverse and long set including 250 drug-like compounds. Based on the calculated descriptors by Dragon software package, to detect أکثرA robust and reliable quantitative structure-property relationship (QSPR) study was established to forecast the melting points (MPs)Â of a diverse and long set including 250 drug-like compounds. Based on the calculated descriptors by Dragon software package, to detect homogeneities and to split the whole dataset into training and test sets, a principal component analysis (PCA) approach was used. Accordingly, there was no outlier in the constructed cluster. Afterwards, the genetic algorithm (GA) feature selection strategy was used to select the most impressive descriptors resulting in the best-fitted models. In addition, multiple linear regression (MLR) and support vector machine (SVM) were used to develop linear and non-linear models correlating the molecular descriptors and the melting points. The validation of the obtained models was confirmed applying cross validation, chance correlation along with statistical features associated with external test set. Our computational study exactly showed a determination coefficient and of 0.853 and a root mean square error (RMSE) of 11.082, which are better than those MLR model (R2=0.712, RMSE 15.042%) accounting for higher capability of SVM-based model in prediction of the theoretical values related to melting points. In fact, using the GA approach resulted in selection of powerful descriptors having useful information concerning effective variables on MPs, which can be utilized in further designing of drug-like compounds with desired melting points. تفاصيل المقالة
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