A 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. Manuscript profile

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. Manuscript profile

Essential Oils are highly concentrated substances the subtle, aromatic and volatile liquids. The use of essential oils is largely widespread in foods, deodorants, pharmaceuticals, drinks, cosmetics, medicine and embalming antiseptics especially with aromatherapy becoming increasingly popular. The lipophilicity of an organic compound can be described by a partition coefficient, logP, which plays a significant role in drug discovery and compound design. A data set of 40 compounds in the essential oil of kesum was randomly divided into 3 groups: training, test and validation sets consisting of 70%, 15% and 15% of data point, respectively. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm - Multiple Linear Regressions (GA-MLR) and genetic algorithm -artificial neural network (GA-ANN) were employed to design the Quantitative Structure-Property Relationship (QSPR) models. The predictive powers of the QSPR model was discussed using Coefficient of determination (R2), Absolute Average Deviation (AAD) and the Mean Squared Error (MSE). The R2 and MSE values of the MLR model were calculated to be 0.734 and 0.194 respectively. The R2 and MSE values for the training set of the ANN model were calculated to be 0.9905 and 2×10-4 respectively. Comparison of the results revealed that the application the GA-ANN method gave better results than GA-MLR method Manuscript profile

A QSPR study on a series of 2-Phenylindole derivatives as anticancer agents was performed to explore the important molecular descriptor which is responsible for their thermodynamic properties such as heat capacity (Cv) and entropy(S).Molecular descriptors were calculated using DRAGON software and the Genetic Algorithm (GA) and backward selection procedure were used to reduce and select the suitable descriptors. Multiple Linear Regression (MLR) analysis was carried out to derive QSPR models, which were further evaluated for statistical significance such as squared correlation coefficient (R2) root mean square error (RMSE), adjusted correlation coefficient (R2adj) and fisher index of quality (F).The multicollinearity of the descriptors selected in the models were tested by calculating the variance inflation factor (VIF), Pearson correlation coefficient (PCC) and the DurbinWatson (DW) statistics. The predictive powers of the MLR models were discussed using Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The best QSPR models for prediction the Cv(J/molK) and S(J/molK), having squared correlation coefficient R2 =0.907 and 0.901, root mean squared error RMSE=2.019 and RMSE= 2.505, and cross-validated squared correlation coefficient R2 cv = 0.902 and 0.889, respectively. The statistical outcomes derived from the present study demonstrate good predictability and may be useful in the design of new 2-Phenylindole derivatives. Manuscript profile

ما در این مطالعه، محاسبات مکانیک کوانتومی را در سطح تئوری تابع چگالی با مجموعه پایه 6-31G* انجام دادیم تا یک مدل رابطه کمی ساختار-سمیت (QSTR) برای پیش‌بینی دوز کشنده (LD50) مشتقات کاربامات‌ها بسازیم. بهترین توصیفگرهای مولکولی با استفاده از الگوریتم ژنتیک (GA) توسط نرم افزار MATLAB انتخاب شدند. سپس، رابطه بین توصیفگرهای انتخاب شده و logLD50 مشتقات کاربامات را با استفاده از مدل های رگرسیون خطی چندگانه گام به گام (BW-MLR) و شبکه عصبی مصنوعی (BP-ANN) مورد مطالعه قرار دادیم. توصیفگرهای RDF010e، WW و R3e برای مدل‌سازی روش‌های GA-BWMLR و GA-BPANN استفاده شدند. مقایسه نتایج نشان داد که R2 و Q2 مدل GA-BPANN برای همه مجموعه ها به طور قابل توجهی بالاتر از مدل GA-BWMLR می باشند. با توجه به مقادیر میانگین مربعات خطای کمتر (MSE)، ریشه میانگین مربع خطا (RMSE)، خطای استاندارد پیش‌بینی (SEP)، و میانگین مطلق انحراف (ADD) مدل GA-BPANN برای مجموعه داده‌ها از دقت بالاتری برای پیش بینی سمیت کارباماتهای مورد مطالعه برخوردار می باشد. Manuscript profile

آلکالوئیدهای کینولین و مشتقات آنها کاربردهای پزشکی و کشاورزی گسترده ای دارند. در این تحقیق از رابطه کمی ساختار-خاصیت (QSPR) برای پیش‌بینی ضریب تقسیم اکتانول-آب 76 مشتق آلکالوئید کینولین کمپتوتسین (CPT) به عنوان حشره کش با استفاده از روش الگوریتم ژنتیک و روش رگرسیون خطی چند متغیره برگشتی و توصیف‌کننده ‌های مولکولی استفاده شده است. برای رسم ساختار شیمیایی ترکیبات مورد مطالعه از نرم افزار گوس ویواستفاده شد. بهینه ‌سازی هندسی ترکیبات توسط نرم‌ افزار گوسین 09 با استفاده از نظریه تابعی چگالی B3YLP با مجموعه پایه G(d,p) 311-6 انجام شد. برای هر یک از ساختارهای بهینه شده توصیف کننده های مولکولی توسط نرم‌افزار دراگون محاسبه گردید. به منظور کاهش و انتخاب بهترین توصیف کننده ها از روش الگوریتم ژنتیک استفاده شد. همبستگی بین توصیف کننده ها در بهترین مدل با استفاده از ضریب پیرسون و ضریب نفوذ پذیری انجام پذیرفت. برای ارزیابی توانایی پیش بینی مدل از انواع مختلف اعتبار سنجی داخلی ، خارجی و ضرایب آماری بهره گرفته شده است. بهترین مدل QSPR با مقدار مجذور ضریب همبستگی ) 901/0(R2 = ، مجذور ضریب همبستگی اعتبار سنجی تقاطعی یکی بیرون) 919 /0= Q2LOO (، و ریشه میانگین مربع خطا )706/0 (RMSE=به دست آمده است. نتایج نشان داد ضرایب آماری و اعتبارسنجی مدل خطی ساخته شده رضایت‌بخش است و لگاریتم ضریب تقسیم اکتانول-آب مشتقات مورد مطالعه تحت تأثیر توصیف کننده خود همبستگی دو بعدی (ATS8e) است. این اطلاعات می تواند برای طراحی مشتقات جدید آلکالوئید کینولین کمپتوتسین (CPT) به عنوان حشره کش مورد استفاده قرار گیرد. Manuscript profile

در این مطالعه، قدرت پیش بینی ضریب تقسیم آب-اکتانل (logP) برای34 نوع از مشتقات پیرتروئیدی با استفاده از رابطه کمی ساختار–خاصیت مورد مطالعه قرار گرفت. مقدار logP پیرتروئیدهای مورد مطالعه با کمک الگوریتم ژنتیک بر اساس روش رگرسیون خطی چندگانه(GA-MLR) مدل‌سازی شد و معلوم گردید که سه توصیفگر موثر GATS4P ، PW3و ZM1V همبستگی معقولی با logP دارند و منجر به ایجاد مدلی با ضریب رگرسیون بالا و خطای کم شدند. ارزیابی توانایی پیش‌بینی logP با مدل (GA-MLR) توسط پارامترهای آماری: R2 = 0.862، R2adj = 0.848، F=62.296و MSE = 0.503 برای مجموعه آزمایشی انجام شد. همچنین مقدار Q2LOO= 0.861 در روش اعتبارسنجی تقاطعی و نیز مقادیر R2 برابر با 0.880 و 0.929 به ترتیب برای سری های آموزش و آزمایش در روش اعتبارسنجی خارجی, همبستگی بسیار خوبی را بین مقادیر تجربی و مقادیر پیش بینی نشان داد. مشخص گردید که مدل MLR در پیش‌بینی logP حشره‌کش‌های پیرتروئیدی قابل اعتماد بوده و با در نظر داشتن خطای بسیار کم از دقت کافی برخوردار است. Manuscript profile

A novel metal–organic framework (MOF), with the formula [Cu(II)L]n (L= 4, 4′-diamino diphenyl sulfone), has been synthesized conventionally and hydrothermally methods and characterized by FT-IR, PXRD, EDX, and SEM techniques. The results MOFs were applied for photodegradation of Methylene Blue (MB). The influence of affecting variables, such as initial MB dye concentration (2–8mg L−1), Cu(II)-MOF mass (0.01–0.03 mg), pH (3.0–9.0), and time of irradiation (30–90 min). The photocatalytic degradation efficiency was investigated by the central composite design (CCD) methodology. The results of CCD analysis for optimum values of variables revealed that Cu(II)-MOF mass was 0.025g, the initial concentration of MB was 3.51 mg L−1, pH was 4.50 and irradiation time was 75 min.Under the optimum conditions, the photocatalytic MB degradation percentage at the desirability function value of 1.0 was found to be 70%. In addition, the obtained R2 value of 0.97 in the regression analysis showed a high photocatalytic efficiency of the proposed method for MB degradation. Manuscript profile

A novel metal-organic framework (MOF), with the formula [Cu(II)L]n (L= 4, 4′-diamino diphenyl sulfone), has been synthesized conventionally and hydrothermally methods and characterized by FT-IR, PXRD, EDX, and SEM techniques. The results MOFs were applied for photodegradation of MethyleneBlue (MB). The influence of affecting variables, such as initial MB dye concentration (2–8mg L−1), Cu(II)-MOF mass (0.01–0.03 mg), pH (3.0–9.0), and time of irradiation (30–90 min). The photocatalytic degradation efficiency was investigated by the central composite design (CCD) methodology. The results of CCD analysis for optimum values of variables revealed that Cu(II)-MOF mass was 0.025g, theinitial concentration of MB was 3.51 mg L−1, pH was 4.50, and irradiation time was 75 min. Under the optimum conditions, the photocatalytic MB degradation percentage at the desirability function value of 1.0 was found to be 70%. In addition, the obtained R2 value of 0.97 in the regression analysis showed a high photocatalytic efficiency of the proposed method for MB degradation. Manuscript profile

A novel metal–organic framework (MOF), with the formula [Cu(II)L]n (L=4, 4′-diamino diphenyl sulfone), has been synthesized conventionally andhydrothermally methods and characterized by FT-IR, PXRD, EDX, and SEMtechniques. The results MOFs were applied for photodegradation of MethyleneBlue (MB). The influence of affecting variables, such as initial MB dyeconcentration (2–8mg L−1), Cu(II)-MOF mass (0.01–0.03 mg), pH (3.0–9.0), andtime of irradiation (30–90 min). The photocatalytic degradation efficiency wasinvestigated by the central composite design (CCD) methodology. The resultsof CCD analysis for optimum values of variables revealed that Cu(II)-MOF masswas 0.025g, the initial concentration of MB was 3.51 mg L−1, pH was 4.50 andirradiation time was 75 min.Under the optimum conditions, the photocatalytic MBdegradation percentage at the desirability function value of 1.0 was found to be70%. In addition, the obtained R2 value of 0.97 in the regression analysis showeda high photocatalytic efficiency of the proposed method for MB degradation. Manuscript profile