shahin ahmadi - Academia.edu (original) (raw)

Papers by shahin ahmadi

RSC advances, 2024

In order to assess the fate and persistence of volatile organic compounds (VOCs) in the atmospher... more In order to assess the fate and persistence of volatile organic compounds (VOCs) in the atmosphere, it is necessary to determine their oxidation rate constants for their reaction with ozone (k O 3). However, given that experimental values of k O 3 are only available for a few hundred compounds and their determination is expensive and time-consuming, developing predictive models for k O 3 is of great importance. Thus, this study aimed to develop reliable quantitative structure-activity relationship (QSAR) models for 302 values of 149 VOCs across a broad temperature range (178-409 K). The model was constructed based on the combination of a simplified molecular-input line-entry system (SMILES) and temperature as an experimental condition, namely quasi-SMILES. In this study, temperature was incorporated in the models as an independent feature. The hybrid optimal descriptor generated from the combination of quasi-SMILES and HFG (hydrogen-filled graph) was used to develop reliable, accurate, and predictive QSAR models employing the CORAL software. The balance between the correlation method and four different target functions (target function without considering IIC or CII, target function using each IIC or CII, and target function based on the combination of IIC and CII) was used to improve the predictability of the QSAR models. The performance of the developed models based on different target functions was compared. The correlation intensity index (CII) significantly enhanced the predictability of the model. The best model was selected based on the numerical value of R m 2 of the calibration set (split #1, R train 2 = 0.9834, R calibration 2 = 0.9276, R validation 2 = 0.9136, and R m 2 calibration = 0.8770). The promoters of increase/decrease for log k O 3 were also computed based on the best model. The presence of a double bond (BOND10000000 and $10 000 000 000), absence of halogen (HALO00000000), and the nearest neighbor codes for carbon equal to 321 (NNC-C/321) are some significant promoters of endpoint increase.

Scientific Reports

Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimer... more Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R2, Q2, and IIC for the validation set of splits 1 to 3 are in the range of 0.7180–0.7...

RSC Advances

In the ecotoxicological risk assessment, acute toxicity is one of the most significant criteria.

RSC Advances

The melting points of imidazolium ILs are studied employing a quantitative structure–property rel... more The melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs.

Journal of Electronic Materials

In this work, the Photo catalytic degradation of Congo red, as an azo dye, was investigated via a... more In this work, the Photo catalytic degradation of Congo red, as an azo dye, was investigated via a ZrO 2 /ZnO nanocomposite (NC) supported on montmorillonite (MMT) clay. The ZnO nanoparticles (NPs), and the ZrO 2 /ZnO, and ZrO 2 /ZnO/clay NCs were prepared by the sol-gel method under ultrasonic irradiation. The morphologies and particle sizes of the synthesized nanomaterials were characterized by field-emission scanning electron microscopy coupled with X-ray dispersive spectroscopy. The particle size of the ZnO NPs, and the ZrO 2 /ZnO, and ZrO 2 /ZnO/clay NCs are 55 nm, 65 nm, and 35 nm, respectively. Fourier transform infrared and X-ray diffraction (XRD) were employed to determine the purity, crystalline phase, and crystallite size of the prepared nanomaterials. The XRD data showed the hexagonal structure of the ZnO and the monoclinic structure of ZrO 2. The results showed that ZrO 2 /ZnO/clay NC is superior in Photo catalytic activity and adsorption efficiency in Congo red degradation. The Photo catalytic property of ZnO/ZrO 2 /MMT (92%) was enhanced to compare the as-synthesized ZnO NPs and ZnO/ZrO 2 NCs. The effect of photocatalyst dosage and initial concentration of Congo red and pH of the solution was investigated. The optimum values were 0.5 g/L of photocatalyst dosage, 10 ppm initial concentration of CR solution, and a pH of 7.0 in the photodegradation process.

Toxicology Mechanisms and Methods

Journal of Molecular Liquids

Nanotoxicology

Abstract Metal oxide nanoparticles (MO-NPs) have unique structural characteristics, exceptionally... more Abstract Metal oxide nanoparticles (MO-NPs) have unique structural characteristics, exceptionally high surface area, strong mechanical stability, catalytic activities, and are biocompatible. Consequently, MO-NPs have recently attracted considerable interest in the field of imaging-guided therapeutic and biosensing applications. This study aims to develop Quantitative Structure–Activity Relationships (QSAR) for the prediction of cell viability of MO-NPs. The QSAR model based on the so-called optimal descriptors which calculated with a simplified molecular input-line entry system (SMILES). The Monte Carlo technique applied to calculate correlation weights for SMILES fragments. Factually, the optimal descriptor for SMILES is the summation of the correlation weights. The model of cytotoxicity is one variable correlation between cytotoxicity and the above optimal descriptor. The Correlation Intensity Index (CII) is a possible criterion of the predictive potential of the model. Applying the CII as a component of the target function in the Monte Carlo optimization routine, employed by the CORAL program, that is designed to find a predictive relationship between the optimal descriptor and cytotoxicity of MO-NPs, improves the statistical quality of the model. The significance of different eclectic features, in terms of whether they increase/decrease cell viability, i.e. decrease or increase cytotoxicity, is also discussed. Numerical data on 83 experimental samples of MO-NPs activity under different conditions taken from the literature are applied for the “nano-QSAR” analysis.

Structural Chemistry

Ionic liquids (ILs) have been popular in many industrial and chemical processes, like antimicrobi... more Ionic liquids (ILs) have been popular in many industrial and chemical processes, like antimicrobial properties, solvents, and synthesis of new compounds with antioxidant activity. Because of the significance of their application, the prediction minimal inhibitory concentration (MIC) of 204 ILs and the minimal bactericidal concentration (MBC) of 114 ILs of them against Staphylococcus aureus (S. aureus) have been carried out using the quantitative structure activity relationship (QSAR) based on the Monte Carlo method. Using the simplified molecular input line entry system (SMILES) notation, molecular structures of all of ILs were displayed. Hybrid optimal descriptor was employed in developing the model for pMIC and pMBC, which was obtained by combining the molecular graph and SMILES. For pMIC, hybrid optimal descriptors were calculated via SMILES and hydrogen-suppressed molecular graph (HSG), as well as hybrid optimal descriptors for pMBC were calculated via SMILES and hydrogen-filled graph (HFG). The total dataset was randomly split into training, invisible training, calibration, and validation set for three times. Statistically analyzed by the calculated descriptors, a QSAR model was developed for pMIC and pMBC of ILs, and the index of ideality of correlation (IIC) was examined as a benchmark for predictive potential of these models. Their correlation coefficient (R2) values of the training, invisible training, calibration, and validation sets for three splits were 0.8585–0.8853, 0.8523–0.8898, 0.8809–0.9240, and 0.8036–0.8903 for pMIC and 0.8357–0.8991, 0.8223–0.9306, 0.8372–0.9170, and 0.8171–0.8901 for pMBC, respectively. The results show that the predictability to develop the QSAR model for all splits is at a high level.

Chemosphere

Several types of metal oxide nanoparticles (MO-NPs) are often utilized as one of the novel class ... more Several types of metal oxide nanoparticles (MO-NPs) are often utilized as one of the novel class of materials in the pharmaceutical industry and human health. The wide use of MO-NPs forces an enhanced understanding of their potential impact on human health and the environment. The research aims to investigate and develop a nano-QFAR (nano-quantitative feature activity relationship) model applying the quasi-SMILES such as cell line, assay, time exposition, concentration, nanoparticles size and metal oxide type for prediction of cell viability (%) of MO-NPs. The total set of 83 quasi-SMILES of MO-NPs divided into training, validation and test sets randomly three times. The statistical model results based on the balance of correlation target function (TF1) and index of ideality correlation target function (TF2) and the Monte Carlo optimization were compared. The comparison of two target function results indicated that TF2 improves the predictability of models. The significance of various eclectic features of both increase and decrease of cell viability (%) is provided. Mechanistic interpretation of significant factors for the model are proposed as well. The sufficient statistical quality of three nano-QFAR models based on TF2 reveals that the developed models can be efficiency for predictions of the cell viability (%) of MO-NPs.

New Journal of Chemistry

In the current study, a sensitive, rapid, accurate and practical procedure is established for det... more In the current study, a sensitive, rapid, accurate and practical procedure is established for determination of atorvastatin (AT) and valsartan (VAS) together from human biological fluids by dispersive micro solid...

IET Nanobiotechnology

In this research, the successful application of polypyrrole (PPy)-modified magnetic nanoparticles... more In this research, the successful application of polypyrrole (PPy)-modified magnetic nanoparticles (NPs) is described as an efficient adsorbent for the extraction and the preconcentration of glibenclamide (GB). To measure it in biological fluids samples, HPLC-UV detection was used. First, iron oxide NPs were prepared by coprecipitation procedure and then their surface was modified by PPy monomers. Characteristics of Fe3O4@PPy NPs were investigated by FTIR technique and NP size studied with scanning electron microscopy. The vibrating sample magnetometer was used to characterise the magnetic properties of the prepared modified NPs. The affecting parameters in extraction including analyte sorption time, analyte desorption time, ionic strength, sample volume, pH, eluent type, eluent amount, and amount of Fe3O4@PPy NPs were investigated and optimised. The linear range of the proposed method is 0.2–700.0 μg l−1 and the limit of detection is 0.1 μg l − 1. The relative standard deviation for five replicate analyses was 3.9. Finally, the proposed procedure was successfully employed for preconcentration and determination of GB in biological fluids.

Journal of Molecular Structure

Abstract The anticancer activity of chalcones and their analogs is the most important biological ... more Abstract The anticancer activity of chalcones and their analogs is the most important biological activity of them among their broad spectrum of their biological activity. In this investigation, we performed quantitative structure–activity relationship (QSAR) modeling of the anticancer activity of 134 chalcones and their analogs against MCF-7 human breast cancer cell lines using Monte Carlo method. QSAR models were calculated by CORAL software and optimal descriptors were calculated with SMILES and hydrogen suppressed molecular graph (HSG). The total dataset split into training, invisible training, calibration, and validation set randomly. Analysis of three probes of the Monte Carlo optimization with three random splits was done. Results from three random splits displayed robust, very simple, predictable, and reliable models for training, invisible training, calibration, and validation set with the correlation coefficient (R2) of 0.8142–0.8244, 0.8244–0.8699, 0.8125–0.8627 and 0.8290–0.8686 respectively. As a result, the obtained models help to identify the hybrid descriptors for the increase and the decrease of anticancer activity of chalcones against MCF-7 human breast cancer cell lines. This simple QSAR model can be used for prediction of log IC50 of numerous chalcone derivatives against breast cancer cell.

Micro & Nano Letters

In the current study, the successful application of polypyrrole-Fe3O4 nanoparticles (NPs) was inv... more In the current study, the successful application of polypyrrole-Fe3O4 nanoparticles (NPs) was investigated as a suitable sorbent in the magnetic-dispersive solid phase extraction mode to the preconcentration and determination of atorvastatin (AT) in human serum by high-performance liquid chromatography-ultraviolet detection. Iron oxide NPs were prepared by co-precipitation method and the polypyrrole compound was used to modify their surface. The structure and NP size of Fe3O4@PPy NPs were characterised by Fourier-transform infrared technique and scanning electron microscopy. Some factors affecting the extraction efficiency, including the pH value, amount of sorbent, extraction time, elution type and its volume and desorption time were optimised. Under the optimum conditions, magnetic NPs extraction of standard solution of AT showed a linear calibration curve in the range of 0.1–1000 μg l−1 with R 2 = 0.9962. The method was sensitive, with a low limit of detection (0.10 μg l−1) and quantification (0.38 μg l−1). The relative standard deviation of five extractions at the concentration level of 0.1 μg l−1 was 4.2. Good recoveries (92.00–98.10%) with low relative standard deviations (6.0–2.4%) indicated that the matrices do not significantly affect the extraction process. Finally, the proposed method was successfully used to measure AT in human serum samples.

Macroheterocycles

The association constant (logK) of 53 chelates of new macrocycles (hemispherands, cryptahemispher... more The association constant (logK) of 53 chelates of new macrocycles (hemispherands, cryptahemispherands, and bridged calix-4) with sodium cation is predicted by a statistically validated QSPR modeling approach. The applied multiple linear regression is based on a variety of theoretical molecular descriptor selected from 6 classes of Dragon software with a forward stepwise multiple linear regression as a feature selection technique. For external validation we applied self organizing maps (SOM) to split the original data set into training and test set. The best four-dimensional model is developed on a training set of 40 macrocycles. The external validation was performed on test set of 13 macrocycles. The QSPR model presented in this study showed good predictions with the leave one out cross validated variance (Q 2 loo-cv = 0.94) and the external-validated variance (Q 2 ext = 0.92). The applicability domain (AD) of the model is analysed by leverage method.

Current Computer-Aided Drug Design

The health and life of humans have been seriously threatened by cancer for a long period and canc... more The health and life of humans have been seriously threatened by cancer for a long period and cancer has become the leading disease-related cause of deaths of human population. Natural products such as colchicine and vinblastine inhibit microtubule assembly by preventing tubulin polymerization. GA-MLR is a powerful search technique based on the evolution of biological systems for QSAR modeling. In this paper, we studied QSAR modeling of some arylthioindole class of colchicine polymerization inhibitors as anticancer agents using GA-MLR and stepwise-MLR. The chemical structures and experimental values for inhibition of colchicine binding taken from the literature. In the study of inhibition of colchicine binding the total numbers of 49 compounds were split into the training and test sets randomly, which have 39 and 10 compounds, respectively. The Chem3D module was used in order to create the 3D structures of compounds; geometry optimization, using the Polak-Ribiere algorithm. The total numbers of 1185 molecular descriptors such as GETAWAY, RDF, WHIM and 3D-MoRSE descriptors were derived for proper characterizing the structures of arylthioindoles derivatives. These molecular descriptors were reduced to 447. In fact the variables which have low correlation with response, constant variables and also collinear descriptors were eliminated. The random sampling of the training set (80% of data) was performed 20 times and the remaining molecules have been used as external validation set. GA-MLR and S-MLR methods were applied on all random training data sets. After splitting the data set by RS method, the GA-MLR and S-MLR methods were applied on the training set to select important variables. The best models consist of one, two, three, four, five and six variables created to find the best QSAR model. The best multivariate linear model based on Q2cal and Q2test values had five parameters in both GA-MLR and S-MLR methods. The results indicate that in this study, the Q2test values are 0.6209 and 0.1144 for GAMLR and S-MLR methods; respectively. According to the results of external validation, we can conclude that the GA-MLR method is more powerful than S-MLR in variable selecting. Also in SAR studies we can conclude that the arylthioindole derivatives with higher density of electrons in C2 position have the largest amounts of IC50. So we can use this important fact to synthesize stronger anticancer agents.

Current drug discovery technologies, Jan 25, 2016

The data set splitting and feature selection are two fundamental steps in QSPR studies. In this c... more The data set splitting and feature selection are two fundamental steps in QSPR studies. In this contribution we have studied on the QSPR modeling of 88 N-aryl derivatives as butyrylcholinesterase inhibitors. At first the data set have been divided to the training and test set for external validation of QSPR model using self-organizing maps and random splitting. The random splitting of training subset (80% of data) performed 20 times and the remaining compounds used as external validation subset. The GA-MLR and S-MLR methods have been applied as variable selection methods on 20 random training and SOM sets. From the 1145 descriptors generated by Dragon program, we selected only five descriptors by preprocessing and then applying the GA- MLR method on data set. The external validation statistics reported for each model served as a basis for the final comparison. The results indicate that SOM data splitting and GA-MLR method can be employed as more reliable methods to develop a predict...

RSC advances, 2024

In order to assess the fate and persistence of volatile organic compounds (VOCs) in the atmospher... more In order to assess the fate and persistence of volatile organic compounds (VOCs) in the atmosphere, it is necessary to determine their oxidation rate constants for their reaction with ozone (k O 3). However, given that experimental values of k O 3 are only available for a few hundred compounds and their determination is expensive and time-consuming, developing predictive models for k O 3 is of great importance. Thus, this study aimed to develop reliable quantitative structure-activity relationship (QSAR) models for 302 values of 149 VOCs across a broad temperature range (178-409 K). The model was constructed based on the combination of a simplified molecular-input line-entry system (SMILES) and temperature as an experimental condition, namely quasi-SMILES. In this study, temperature was incorporated in the models as an independent feature. The hybrid optimal descriptor generated from the combination of quasi-SMILES and HFG (hydrogen-filled graph) was used to develop reliable, accurate, and predictive QSAR models employing the CORAL software. The balance between the correlation method and four different target functions (target function without considering IIC or CII, target function using each IIC or CII, and target function based on the combination of IIC and CII) was used to improve the predictability of the QSAR models. The performance of the developed models based on different target functions was compared. The correlation intensity index (CII) significantly enhanced the predictability of the model. The best model was selected based on the numerical value of R m 2 of the calibration set (split #1, R train 2 = 0.9834, R calibration 2 = 0.9276, R validation 2 = 0.9136, and R m 2 calibration = 0.8770). The promoters of increase/decrease for log k O 3 were also computed based on the best model. The presence of a double bond (BOND10000000 and $10 000 000 000), absence of halogen (HALO00000000), and the nearest neighbor codes for carbon equal to 321 (NNC-C/321) are some significant promoters of endpoint increase.

Scientific Reports

Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimer... more Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R2, Q2, and IIC for the validation set of splits 1 to 3 are in the range of 0.7180–0.7...

RSC Advances

In the ecotoxicological risk assessment, acute toxicity is one of the most significant criteria.

RSC Advances

The melting points of imidazolium ILs are studied employing a quantitative structure–property rel... more The melting points of imidazolium ILs are studied employing a quantitative structure–property relationship (QSPR) approach to develop a model for predicting the melting points of a data set of imidazolium ILs.

Journal of Electronic Materials

In this work, the Photo catalytic degradation of Congo red, as an azo dye, was investigated via a... more In this work, the Photo catalytic degradation of Congo red, as an azo dye, was investigated via a ZrO 2 /ZnO nanocomposite (NC) supported on montmorillonite (MMT) clay. The ZnO nanoparticles (NPs), and the ZrO 2 /ZnO, and ZrO 2 /ZnO/clay NCs were prepared by the sol-gel method under ultrasonic irradiation. The morphologies and particle sizes of the synthesized nanomaterials were characterized by field-emission scanning electron microscopy coupled with X-ray dispersive spectroscopy. The particle size of the ZnO NPs, and the ZrO 2 /ZnO, and ZrO 2 /ZnO/clay NCs are 55 nm, 65 nm, and 35 nm, respectively. Fourier transform infrared and X-ray diffraction (XRD) were employed to determine the purity, crystalline phase, and crystallite size of the prepared nanomaterials. The XRD data showed the hexagonal structure of the ZnO and the monoclinic structure of ZrO 2. The results showed that ZrO 2 /ZnO/clay NC is superior in Photo catalytic activity and adsorption efficiency in Congo red degradation. The Photo catalytic property of ZnO/ZrO 2 /MMT (92%) was enhanced to compare the as-synthesized ZnO NPs and ZnO/ZrO 2 NCs. The effect of photocatalyst dosage and initial concentration of Congo red and pH of the solution was investigated. The optimum values were 0.5 g/L of photocatalyst dosage, 10 ppm initial concentration of CR solution, and a pH of 7.0 in the photodegradation process.

Toxicology Mechanisms and Methods

Journal of Molecular Liquids

Nanotoxicology

Abstract Metal oxide nanoparticles (MO-NPs) have unique structural characteristics, exceptionally... more Abstract Metal oxide nanoparticles (MO-NPs) have unique structural characteristics, exceptionally high surface area, strong mechanical stability, catalytic activities, and are biocompatible. Consequently, MO-NPs have recently attracted considerable interest in the field of imaging-guided therapeutic and biosensing applications. This study aims to develop Quantitative Structure–Activity Relationships (QSAR) for the prediction of cell viability of MO-NPs. The QSAR model based on the so-called optimal descriptors which calculated with a simplified molecular input-line entry system (SMILES). The Monte Carlo technique applied to calculate correlation weights for SMILES fragments. Factually, the optimal descriptor for SMILES is the summation of the correlation weights. The model of cytotoxicity is one variable correlation between cytotoxicity and the above optimal descriptor. The Correlation Intensity Index (CII) is a possible criterion of the predictive potential of the model. Applying the CII as a component of the target function in the Monte Carlo optimization routine, employed by the CORAL program, that is designed to find a predictive relationship between the optimal descriptor and cytotoxicity of MO-NPs, improves the statistical quality of the model. The significance of different eclectic features, in terms of whether they increase/decrease cell viability, i.e. decrease or increase cytotoxicity, is also discussed. Numerical data on 83 experimental samples of MO-NPs activity under different conditions taken from the literature are applied for the “nano-QSAR” analysis.

Structural Chemistry

Ionic liquids (ILs) have been popular in many industrial and chemical processes, like antimicrobi... more Ionic liquids (ILs) have been popular in many industrial and chemical processes, like antimicrobial properties, solvents, and synthesis of new compounds with antioxidant activity. Because of the significance of their application, the prediction minimal inhibitory concentration (MIC) of 204 ILs and the minimal bactericidal concentration (MBC) of 114 ILs of them against Staphylococcus aureus (S. aureus) have been carried out using the quantitative structure activity relationship (QSAR) based on the Monte Carlo method. Using the simplified molecular input line entry system (SMILES) notation, molecular structures of all of ILs were displayed. Hybrid optimal descriptor was employed in developing the model for pMIC and pMBC, which was obtained by combining the molecular graph and SMILES. For pMIC, hybrid optimal descriptors were calculated via SMILES and hydrogen-suppressed molecular graph (HSG), as well as hybrid optimal descriptors for pMBC were calculated via SMILES and hydrogen-filled graph (HFG). The total dataset was randomly split into training, invisible training, calibration, and validation set for three times. Statistically analyzed by the calculated descriptors, a QSAR model was developed for pMIC and pMBC of ILs, and the index of ideality of correlation (IIC) was examined as a benchmark for predictive potential of these models. Their correlation coefficient (R2) values of the training, invisible training, calibration, and validation sets for three splits were 0.8585–0.8853, 0.8523–0.8898, 0.8809–0.9240, and 0.8036–0.8903 for pMIC and 0.8357–0.8991, 0.8223–0.9306, 0.8372–0.9170, and 0.8171–0.8901 for pMBC, respectively. The results show that the predictability to develop the QSAR model for all splits is at a high level.

Chemosphere

Several types of metal oxide nanoparticles (MO-NPs) are often utilized as one of the novel class ... more Several types of metal oxide nanoparticles (MO-NPs) are often utilized as one of the novel class of materials in the pharmaceutical industry and human health. The wide use of MO-NPs forces an enhanced understanding of their potential impact on human health and the environment. The research aims to investigate and develop a nano-QFAR (nano-quantitative feature activity relationship) model applying the quasi-SMILES such as cell line, assay, time exposition, concentration, nanoparticles size and metal oxide type for prediction of cell viability (%) of MO-NPs. The total set of 83 quasi-SMILES of MO-NPs divided into training, validation and test sets randomly three times. The statistical model results based on the balance of correlation target function (TF1) and index of ideality correlation target function (TF2) and the Monte Carlo optimization were compared. The comparison of two target function results indicated that TF2 improves the predictability of models. The significance of various eclectic features of both increase and decrease of cell viability (%) is provided. Mechanistic interpretation of significant factors for the model are proposed as well. The sufficient statistical quality of three nano-QFAR models based on TF2 reveals that the developed models can be efficiency for predictions of the cell viability (%) of MO-NPs.

New Journal of Chemistry

In the current study, a sensitive, rapid, accurate and practical procedure is established for det... more In the current study, a sensitive, rapid, accurate and practical procedure is established for determination of atorvastatin (AT) and valsartan (VAS) together from human biological fluids by dispersive micro solid...

IET Nanobiotechnology

In this research, the successful application of polypyrrole (PPy)-modified magnetic nanoparticles... more In this research, the successful application of polypyrrole (PPy)-modified magnetic nanoparticles (NPs) is described as an efficient adsorbent for the extraction and the preconcentration of glibenclamide (GB). To measure it in biological fluids samples, HPLC-UV detection was used. First, iron oxide NPs were prepared by coprecipitation procedure and then their surface was modified by PPy monomers. Characteristics of Fe3O4@PPy NPs were investigated by FTIR technique and NP size studied with scanning electron microscopy. The vibrating sample magnetometer was used to characterise the magnetic properties of the prepared modified NPs. The affecting parameters in extraction including analyte sorption time, analyte desorption time, ionic strength, sample volume, pH, eluent type, eluent amount, and amount of Fe3O4@PPy NPs were investigated and optimised. The linear range of the proposed method is 0.2–700.0 μg l−1 and the limit of detection is 0.1 μg l − 1. The relative standard deviation for five replicate analyses was 3.9. Finally, the proposed procedure was successfully employed for preconcentration and determination of GB in biological fluids.

Journal of Molecular Structure

Abstract The anticancer activity of chalcones and their analogs is the most important biological ... more Abstract The anticancer activity of chalcones and their analogs is the most important biological activity of them among their broad spectrum of their biological activity. In this investigation, we performed quantitative structure–activity relationship (QSAR) modeling of the anticancer activity of 134 chalcones and their analogs against MCF-7 human breast cancer cell lines using Monte Carlo method. QSAR models were calculated by CORAL software and optimal descriptors were calculated with SMILES and hydrogen suppressed molecular graph (HSG). The total dataset split into training, invisible training, calibration, and validation set randomly. Analysis of three probes of the Monte Carlo optimization with three random splits was done. Results from three random splits displayed robust, very simple, predictable, and reliable models for training, invisible training, calibration, and validation set with the correlation coefficient (R2) of 0.8142–0.8244, 0.8244–0.8699, 0.8125–0.8627 and 0.8290–0.8686 respectively. As a result, the obtained models help to identify the hybrid descriptors for the increase and the decrease of anticancer activity of chalcones against MCF-7 human breast cancer cell lines. This simple QSAR model can be used for prediction of log IC50 of numerous chalcone derivatives against breast cancer cell.

Micro & Nano Letters

In the current study, the successful application of polypyrrole-Fe3O4 nanoparticles (NPs) was inv... more In the current study, the successful application of polypyrrole-Fe3O4 nanoparticles (NPs) was investigated as a suitable sorbent in the magnetic-dispersive solid phase extraction mode to the preconcentration and determination of atorvastatin (AT) in human serum by high-performance liquid chromatography-ultraviolet detection. Iron oxide NPs were prepared by co-precipitation method and the polypyrrole compound was used to modify their surface. The structure and NP size of Fe3O4@PPy NPs were characterised by Fourier-transform infrared technique and scanning electron microscopy. Some factors affecting the extraction efficiency, including the pH value, amount of sorbent, extraction time, elution type and its volume and desorption time were optimised. Under the optimum conditions, magnetic NPs extraction of standard solution of AT showed a linear calibration curve in the range of 0.1–1000 μg l−1 with R 2 = 0.9962. The method was sensitive, with a low limit of detection (0.10 μg l−1) and quantification (0.38 μg l−1). The relative standard deviation of five extractions at the concentration level of 0.1 μg l−1 was 4.2. Good recoveries (92.00–98.10%) with low relative standard deviations (6.0–2.4%) indicated that the matrices do not significantly affect the extraction process. Finally, the proposed method was successfully used to measure AT in human serum samples.

Macroheterocycles

The association constant (logK) of 53 chelates of new macrocycles (hemispherands, cryptahemispher... more The association constant (logK) of 53 chelates of new macrocycles (hemispherands, cryptahemispherands, and bridged calix-4) with sodium cation is predicted by a statistically validated QSPR modeling approach. The applied multiple linear regression is based on a variety of theoretical molecular descriptor selected from 6 classes of Dragon software with a forward stepwise multiple linear regression as a feature selection technique. For external validation we applied self organizing maps (SOM) to split the original data set into training and test set. The best four-dimensional model is developed on a training set of 40 macrocycles. The external validation was performed on test set of 13 macrocycles. The QSPR model presented in this study showed good predictions with the leave one out cross validated variance (Q 2 loo-cv = 0.94) and the external-validated variance (Q 2 ext = 0.92). The applicability domain (AD) of the model is analysed by leverage method.

Current Computer-Aided Drug Design

The health and life of humans have been seriously threatened by cancer for a long period and canc... more The health and life of humans have been seriously threatened by cancer for a long period and cancer has become the leading disease-related cause of deaths of human population. Natural products such as colchicine and vinblastine inhibit microtubule assembly by preventing tubulin polymerization. GA-MLR is a powerful search technique based on the evolution of biological systems for QSAR modeling. In this paper, we studied QSAR modeling of some arylthioindole class of colchicine polymerization inhibitors as anticancer agents using GA-MLR and stepwise-MLR. The chemical structures and experimental values for inhibition of colchicine binding taken from the literature. In the study of inhibition of colchicine binding the total numbers of 49 compounds were split into the training and test sets randomly, which have 39 and 10 compounds, respectively. The Chem3D module was used in order to create the 3D structures of compounds; geometry optimization, using the Polak-Ribiere algorithm. The total numbers of 1185 molecular descriptors such as GETAWAY, RDF, WHIM and 3D-MoRSE descriptors were derived for proper characterizing the structures of arylthioindoles derivatives. These molecular descriptors were reduced to 447. In fact the variables which have low correlation with response, constant variables and also collinear descriptors were eliminated. The random sampling of the training set (80% of data) was performed 20 times and the remaining molecules have been used as external validation set. GA-MLR and S-MLR methods were applied on all random training data sets. After splitting the data set by RS method, the GA-MLR and S-MLR methods were applied on the training set to select important variables. The best models consist of one, two, three, four, five and six variables created to find the best QSAR model. The best multivariate linear model based on Q2cal and Q2test values had five parameters in both GA-MLR and S-MLR methods. The results indicate that in this study, the Q2test values are 0.6209 and 0.1144 for GAMLR and S-MLR methods; respectively. According to the results of external validation, we can conclude that the GA-MLR method is more powerful than S-MLR in variable selecting. Also in SAR studies we can conclude that the arylthioindole derivatives with higher density of electrons in C2 position have the largest amounts of IC50. So we can use this important fact to synthesize stronger anticancer agents.

Current drug discovery technologies, Jan 25, 2016

The data set splitting and feature selection are two fundamental steps in QSPR studies. In this c... more The data set splitting and feature selection are two fundamental steps in QSPR studies. In this contribution we have studied on the QSPR modeling of 88 N-aryl derivatives as butyrylcholinesterase inhibitors. At first the data set have been divided to the training and test set for external validation of QSPR model using self-organizing maps and random splitting. The random splitting of training subset (80% of data) performed 20 times and the remaining compounds used as external validation subset. The GA-MLR and S-MLR methods have been applied as variable selection methods on 20 random training and SOM sets. From the 1145 descriptors generated by Dragon program, we selected only five descriptors by preprocessing and then applying the GA- MLR method on data set. The external validation statistics reported for each model served as a basis for the final comparison. The results indicate that SOM data splitting and GA-MLR method can be employed as more reliable methods to develop a predict...