In silico aquatic toxicity prediction of chemicals toward Daphnia magna and fathead minnow using Monte Carlo approaches.

The fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. Daphnia magna and Fathead minnow are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants. Herein, global quantitative structure-toxicity relationship (QSTR) models have been developed to predict the toxicity (pEC(LC)50) of a large dataset comprising 2106 chemicals toward Daphnia magna and Fathead minnow. The optimal descriptor of correlation weights (DCWs) is calculated using the notation of simplified molecular input line entry system (SMILES) and is used to construct QSTR models. Three target functions, TF1, TF2, and TF3 are utilized to generate 12 QSTR models from four splits, and their statistical characteristics are also compared. The designed QSTR models are validated using both internal and external validation criteria and are found to be reliable, robust, and excellently predictive. Among the models, those generated using the TF3 demonstrate the best statistical quality with R2 values ranging from 0.9467 to 0.9607, Q2 values ranging from 0.9462 to 0.9603 and RMSE values ranging from 0.3764 to 0.4413 for the validation set. The applicability domain and the mechanistic interpretations of generated models were also discussed.

DFT based QSAR study on quinolone-triazole derivatives as antibacterial agents.

QSAR modeling was performed on 39 quinolone-triazole derivatives against gram-positive Staphylococcus aureus and gram-negative Pseudomonas aeruginosa bacteria. The molecular structures were optimized using the DFT/B3LYP method and 6-31 G basis set. Molecular descriptors were extracted using quantum mechanical calculations. The hierarchical cluster analysis was performed for a rational subset division. The initial dataset was divided into calibration and validation sets, and modeling was done by stepwise MLR method for each of the two bacteria. Internal and external validation methods confirmed the robustness and predictability of the obtained models. According to the obtained model for S. aureus (R2 = 0.889, R2ext = 0.938, Q2LOO = 0.853), the four descriptors- partial atomic charges for the N1 atom in triazole and C7 of the quinolone nucleus, 4-carbonyl bond length, and 13C-NMR chemical shift of 3-carboxylic acid- were found to be the descriptors controlling the activity. According to the obtained model for P. aeruginosa (R2 = 0.957, R2ext = 0.923, Q2LOO = 0.909), the O atom's partial charge in carbonyl, LUMO-HOMO energy gap, and logP were found to be the descriptors having the highest correlation with the antibacterial activity. Finally, some new compounds with higher activities were designed and proposed.

SMILES-based QSAR and molecular docking study of xanthone derivatives as α-glucosidase inhibitors.

Increasing diabetic population is one of the major health concerns all over the world. Inhibition of α-glucosidase is a clinically proved and attractive strategy to manage diabetes. In this study, robust and reliable QSAR models to predict α-glucosidase inhibitory potential of xanthone derivatives are developed by the Monte Carlo technique. The chemical structures are represented by SMILES notation without any 3D-optimization. The significance of the index of ideality correlation (IIC) with applicability domain (AD) is also studied in depth. The models developed using CORAL software by considering IIC criteria are found to be statistically more significant and robust than simple balance of correlation. The QSAR models are validated by both internal and external validation methods. The promoters of increase and decrease of activity are also extracted and interpreted in detail. The interpretation of developed models explains the role of different structural attributes in predicting the pIC50 of xanthone derivatives as α-glucosidase inhibitors. Based on the results of model interpretation, modifications are done on some xanthone derivatives and 15 new molecules are designed. The α-glucosidase inhibitory activity of novel molecules is further supported by docking studies.

Quantitative structure-toxicity relationship models for predication of toxicity of ionic liquids toward leukemia rat cell line IPC-81 based on index of ideality of correlation.

The application of ionic liquids (ILs) as green solvents has attracted the attention of the scientific community. However, ILs may play the role of toxins. Even though ionic liquids may assist to minimize air pollution, but their discharge into aquatic ecosystems might result in significant water pollution due to their potential toxicity and inaccessibility to biodegradation. Recently, more attention has been paid to the toxicity of ILs on plants, bacteria, and humans. Here, a quantitative structure-toxicity relationship study (QSTR) based on the Monte Carlo method of CORAL software has been applied to estimate the logarithm of the half-maximal effective concentration of toxicity of ILs against leukemia rat cell line IPC-81 (logEC50). A hybrid optimal descriptor is used to build QSTR models for a large set of 304 diverse ILs including ammonium, imidazolium, morpholinium, phosphonium, piperidinium, pyridinium, pyrrolidinium, quinolinium, sulfonium, and protic ILs. The SMILES notations of ILs are utilized to compute the descriptor correlation weight (DCW). Four splits are made from the whole dataset and each split is randomly divided into four sets (training subsets and validation set). The index of ideality of correlation (IIC) is applied to evaluate the authenticity and robustness of the QSTR models. A QSTR model with statistical parameters R2 = 0.85, CCC = 0.92, Q2 = 0.84, and MAE = 0.25 for the validation set of the best split is considered as a prime model. The outliers and promoters of increase/decrease of logEC50 are extracted and the mechanistic interpretation of effective descriptors for the model is also offered.HighlightsGlobal SMILES-based QSAR model was developed to predict the toxicity of ILs.The CORAL software is used to model the ILs toxicity on IPC-81 leukemia rat cell line.IIC is tested as a criterion of predictive potential.The toxicological effects of ILs are discussed based on the proposed model.

Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method.

In this research, QSAR modeling was carried out through SMILES of compounds and on the basis of the Monte Carlo method to predict the antioxidant activity of 79 derivatives of pulvinic acid, 23 of coumarine, as well as nine structurally non-related compounds against three radiation sources of Fenton, gamma, and UV. QSAR model was designed through CORAL software, as well as a newer optimizing method well known as the index of ideality correlation. The full set of antioxidant compounds were randomly distributed into four sets, including training, invisible training, validation, and calibration; this division was repeated three times randomly. The optimal descriptors were picked up from a hybrid model by the combination of the hydrogen-suppressed graph and SMILES descriptors based on the objective function. These models' predictability was assessed on the sets of validation. The results of three randomized sets showed that simple, robust, reliable, and predictive models were achieved for training, invisible training, validation, and calibration sets of all three models. The central decrease/increase descriptors were identified. This simple QSAR can be useful to predict antioxidant activity of numerous antioxidants.

Thermotolerance and Cellulolytic Activity of Fungi Isolated from Soils/Waste Materials in the Industrial Region of Nigeria.

The current study aimed on isolating thermotolerant, cellulolytic fungi from different tropical soil/waste materials samples such as wood waste, sawmill, decomposing straw and compost pit sites in Abraka, Southern Nigeria and assessing their applications in diverse cellulolytic processes. Fungal isolates were identified based on cultural, morphological, ITS-5.8S barcoding, reproductive structures and thereafter screened for thermotolerance and cellulolytic activities [carboxy methyl cellulase (CMC-ase) and filter paperase (FP-ase)] by cultivating at 45, 50, 60, 70, 80° and 45 °C, respectively. The highest fungal abundance (44.4%) was observed in the compost pit while the lowest (11.1%) was recorded for sawmill. Nine thermotolerant fungal isolates were identified: Aspergillus flavus (4), Blakeslea sp. (3), and Trichoderma asperellum (2). Among them only five, including three A. flavus, one Blakeslea sp. and one T. asperellum, exhibited cellulolytic activity ranging from 12.11 ± 0.01 to 18.42 ± 5.39 µg/mL and 0.36 ± 0.01-9.21 ± 2.52 µg/mL for CMC-ase and filter paperase FP-ase assay, respectively. The low Michaelis-Menten constants of 1.137 for CMC-ase and 1.195 for FP-ase were obtained, indicated a strong affinity for the substrate. The thermotolerance coupled with cellulolytic activity of these isolates make them attractive for potential application in industries where they can be of economic and environmental benefits as against the use of chemicals.

A simple and reliable QSPR model for prediction of chromatography retention indices of volatile organic compounds in peppers.

Worldwide, various types of pepper are used in food as an additive due to their unique pungency, aroma, taste, and color. This spice is valued for its pungency contributed by the alkaloid piperine and aroma attributed to volatile essential oils. The essential oils are composed of volatile organic compounds (VOCs) in different concentrations and ratios. In chromatography, the identification of compounds is done by comparing obtained peaks with a reference standard. However, there are cases where reference standards are either unavailable or the chemical information of VOCs is not documented in reference libraries. To overcome these limitations, theoretical methodologies are applied to estimate the retention indices (RIs) of new VOCs. The aim of the present work is to develop a reliable QSPR model for the RIs of 273 identified VOCs of different types of pepper. Experimental retention indices were measured using comprehensive two-dimensional gas chromatography coupled to quadrupole mass spectrometry (GC × GC/qMS) using a coupled BPX5 and BP20 column system. The inbuilt Monte Carlo algorithm of CORAL software is used to generate QSPR models using the hybrid optimal descriptor extracted from a combination of SMILES and HFG (hydrogen-filled graph). The whole dataset of 273 VOCs is used to make ten splits, each of which is further divided into four sets: active training, passive training, calibration, and validation. The balance of correlation method with four target functions i.e. TF0 (WIIC = WCII = 0), TF1 (WIIC = 0.5 & WCII = 0), TF2 (WIIC = 0 & WCII = 0.3) and TF3 (WIIC = 0.5 & WCII = 0.3) is used. The results of the statistical parameters of each target function are compared with each other. The simultaneous application of the index of ideality of correlation (IIC) and correlation intensity index (CII) improves the predictive potential of the model. The best model is judged on the basis of the numerical value of R2 of the validation set. The statistical result of the best model for the validation set of split 6 computed with TF3 (WIIC = 0.5 & WCII = 0.3) is R2 = 0.9308, CCC = 0.9588, IIC = 0.7704, CII = 0.9549, Q2 = 0.9281 and RMSE = 0.544. The promoters of increase/decrease for RI are also extracted using the best model (split 6). Moreover, the proposed model was used for an external validation set.

QSAR models for the ozonation of diverse volatile organic compounds at different temperatures.

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 (kO3). However, given that experimental values of kO3 are only available for a few hundred compounds and their determination is expensive and time-consuming, developing predictive models for kO3 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 Rm2 of the calibration set (split #1, Rtrain2 = 0.9834, Rcalibration2 = 0.9276, Rvalidation2 = 0.9136, and calibration = 0.8770). The promoters of increase/decrease for log kO3 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.

SMILES-based QSAR virtual screening to identify potential therapeutics for COVID-19 by targeting 3CLpro and RdRp viral proteins.

The COVID-19 pandemic has prompted the medical systems of many countries to develop effective treatments to combat the high rate of infection and death caused by the disease. Within the array of proteins found in SARS-CoV-2, the 3 chymotrypsin-like protease (3CLpro) holds significance as it plays a crucial role in cleaving polyprotein peptides into distinct functional nonstructural proteins. Meanwhile, RNA-dependent RNA polymerase (RdRp) takes center stage as the key enzyme tasked with replicating the viral genomic RNA within host cells. These proteins, 3CLpro and RdRp, are deemed optimal subjects for QSAR modeling due to their pivotal functions in the viral lifecycle. In this study, SMILES-based QSAR classification models were developed for a dataset of 2377 compounds that were defined as either active or inactive against 3CLpro and RdRp. Pharmacophore (PH4) and QSAR modeling were used for the virtual screening on 60.2 million compounds including ZINC, ChEMBL, Molport, and MCULE databases to identify new potent inhibitors against 3CLpro and RdRp. Then, a filter was established based on typical molecular characteristics to identify drug-like molecules. The molecular docking was also performed to evaluate the binding affinity of 156 AND 51 potential inhibitors to 3CLpro and RdRp, respectively. Among the 15 hits identified based on molecular docking scores, M3, N2, and N4 were identified as promising inhibitors due to their good synthetic accessibility scores (3.07, 3.11, and 3.29 out of 10 for M3, N2, and N4 respectively). These compounds contain amine functional groups, which are known for their crucial role in the binding interactions between drugs and their targets. Consequently, these hits have been chosen for further biological assay studies to validate their activity. They may represent novel 3CLpro and RdRp inhibitors possessing drug-like properties suitable for COVID-19 therapy.

In silico study of natural antioxidants.

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.

QSAR and molecular docking studies of isatin and indole derivatives as SARS 3CLpro inhibitors.

The 3C-like protease (3CLpro), known as the main protease of SARS-COV, plays a vital role in the viral replication cycle and is a critical target for the development of SARS inhibitor. Comparative sequence analysis has shown that the 3CLpro of two coronaviruses, SARS-CoV-2 and SARS-CoV, show high structural similarity, and several common features are shared among the substrates of 3CLpro in different coronaviruses. The goal of this study is the development of validated QSAR models by CORAL software and Monte Carlo optimization to predict the inhibitory activity of 81 isatin and indole-based compounds against SARS CoV 3CLpro. The models were built using a newer objective function optimization of this software, known as the index of ideality correlation (IIC), which provides favorable results. The entire set of molecules was randomly divided into four sets including: active training, passive training, calibration and validation sets. The optimal descriptors were selected from the hybrid model by combining SMILES and hydrogen suppressed graph (HSG) based on the objective function. According to the model interpretation results, eight synthesized compounds were extracted and introduced from the ChEMBL database as good SARS CoV 3CLpro inhibitor. Also, the activity of the introduced molecules further was supported by docking studies using 3CLpro of both SARS-COV-1 and SARS-COV-2. Based on the results of ADMET and OPE study, compounds CHEMBL4458417 and CHEMBL4565907 both containing an indole scaffold with the positive values of drug-likeness and the highest drug-score can be introduced as selected leads.

In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization.

The QSAR models are employed to predict the anti-proliferative activity of 81 derivatives of flavonol against prostate cancer using the Monte Carlo algorithm based on the index of ideality of correlation (IIC) criterion. CORAL software is employed to design the QSAR models. The molecular structures of flavonols are demonstrated using the simplified molecular input line entry system (SMILES) notation. The models are developed with the hybrid optimal descriptors i.e. using both SMILES and hydrogen-suppressed molecular graph (HSG). The QSAR model developed for split 3 is selected as a prominent model ([Formula: see text]= 0.727, [Formula: see text]= 0.628, [Formula: see text]= 0.642, and [Formula: see text]=0.615). The model is interpreted mechanistically by identifying the characteristics responsible for the promoter of the increase or decrease. The structural attributes as promoters of increase of pIC50 were aliphatic carbon atom connected to double-bound (C…=…, aliphatic oxygen atom connected to aliphatic carbon (O…C…), branching on aromatic ring (c…(…), and aliphatic nitrogen (N…). The pIC50 of eight natural flavonols with pIC50 more than 4.0, were predicted by the best model. The molecular docking is also performed for natural flavonols on the PC-3 cell line using the protein (PDB: 3RUK).

Quantitative structure-activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes.

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 0.7180-0.7755, 0.6891-0.7561, and 0.4431-0.8611 respectively. The numerical result of [Formula: see text] > 0.5 for all three constructed models in the Y-randomization test validate the reliability of established models. The promoters of increase/decrease for pIC50 are recognized and used for the mechanistic interpretation of structural attributes.

Ecotoxicological prediction of organic chemicals toward Pseudokirchneriella subcapitata by Monte Carlo approach.

In the ecotoxicological risk assessment, acute toxicity is one of the most significant criteria. Green alga Pseudokirchneriella subcapitata has been used for ecotoxicological studies to assess the toxicity of different toxic chemicals in freshwater. Quantitative Structure Activity Relationships (QSAR) are mathematical models to relate chemical structure and activity/physicochemical properties of chemicals quantitatively. Herein, Quantitative Structure Toxicity Relationship (QSTR) modeling is applied to assess the toxicity of a data set of 334 different chemicals on Pseudokirchneriella subcapitata, in terms of EC10 and EC50 values. The QSTR models are established using CORAL software by utilizing the target function (TF2) with the index of ideality of correlation (IIC). A hybrid optimal descriptor computed from SMILES and molecular hydrogen-suppressed graphs (HSG) is employed to construct QSTR models. The results of various statistical parameters of the QSTR model developed for pEC10 and pEC50 range from excellent to good and are in line with the standard parameters. The models prepared with IIC for Split 3 are chosen as the best model for both endpoints (pEC10 and pEC50). The numerical value of the determination coefficient of the validation set of split 3 for the endpoint pEC10 is 0.7849 and for the endpoint pEC50, it is 0.8150. The structural fractions accountable for the toxicity of chemicals are also extracted. The hydrophilic attributes like 1…n…(… and S…(…[double bond, length as m-dash]… exert positive contributions to controlling the aquatic toxicity and reducing algal toxicity, whereas attributes such as c…c…c…, C…C…C… enhance lipophilicity of the molecules and consequently enhance algal toxicity.

Correction: Ecotoxicological prediction of organic chemicals toward Pseudokirchneriella subcapitata by Monte Carlo approach.

[This corrects the article DOI: 10.1039/D2RA03936B.].

Green synthesis of zero-valent iron nanoparticles and loading effect on activated carbon for furfural adsorption.

The adsorption techniques are extensively used in dyes, metronidazole, aniline, wastewater treatment methods to remove certain pollutants. Furfural is organic in nature, considered a pollutant having a toxic effect on humans and their environment and especially aquatic species. Due to distinct characteristics of the adsorption technique, this technique can be utilized to adsorb furfural efficiently. As an environmentally friendly technique, the pomegranate peel was used to synthesized activated carbon and nanostructure of zerovalent iron impregnated on the synthesized activated carbon. The physicochemical and crystallinity characterization was done using Fourier transmission infrared spectroscopy (FTIR), X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET), and Field emission scanning electron microscopy (FESEM). The nanoparticles are porous in structure having 821.74 m2/g specified surface area. The maximum amount of the adsorbent pores in the range of 3.08 nm shows the microporous structure and enhancement in adsorption capacity. The effects of increment in concentration of adsorbent, pH, reaction contact time and adsorbent dose, isothermal and kinetic behaviour were investigated. At the UV wavelength of 227 nm furfural adsorption was detected. The separation of the furfural from the aqueous solution was calculated at the 1 h reaction time at the composite dosage of 4 g/L, 250 mg/L adsorbent concentration and pH kept at 7. The 81.87% is the maximum removal attained by the nanocomposite in comparison to the activated carbon is 62.06%. Furfural adsorption was also analyzed by using the equations of isothermal and kinetics models. The adsorption process analysis depends on the Freundlich isotherm and Intra-particle diffusion than the other models. The maximum adsorbent of the composite was determined by the Langmuir model which is 222.22 mg/g. The furfural removal enhances as the adsorbent dose enhances. The developed zerovalent iron nanoparticles incorporated on activated carbon (AC/nZVI) from pomegranate peel extract are feasible as an efficient and inexpensive adsorbent to eliminate furfural from a liquid solution.

The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors.

Ionic liquids (ILs) have captured intensive attention owing to their unique properties such as high thermal stability, negligible vapour pressure, high dissolution capacity and high ionic conductivity as well as their wide applications in various scientific fields including organic synthesis, catalysis, and industrial extraction processes. Many applications of ionic liquids (ILs) rely on the melting point (T m). Therefore, in the present manuscript, 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. The Monte Carlo algorithm of CORAL software is applied to build up a robust QSPR model to calculate the values T m of 353 imidazolium ILs. Using a combination of SMILES and hydrogen-suppressed molecular graphs (HSGs), the hybrid optimal descriptor is computed and used to generate the QSPR models. Internal and external validation parameters are also employed to evaluate the predictability and reliability of the QSPR model. Four splits are prepared from the dataset and each split is randomly distributed into four sets i.e. training set (≈33%), invisible training set (≈31%), calibration set (≈16%) and validation set (≈20%). In QSPR modelling, the numerical values of various statistical features of the validation sets such as R Validation 2, Q Validation 2, and IICValidation are found to be in the range of 0.7846-0.8535, 0.7687-0.8423 and 0.7424-0.8982, respectively. For mechanistic interpretation, the structural attributes which are responsible for the increase/decrease of T m are also extracted.

Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria.

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.

Correlation intensity index: mathematical modeling of cytotoxicity of metal oxide nanoparticles.

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.

Hydrothermal synthesis of LaFeO3 nanoparticles adsorbent: Characterization and application of error functions for adsorption of fluoride.

The adsorption of fluoride from aqueous solution by lanthanum ferrite nanoparticles (LaFeO3 NPs) synthesized by the hydrothermal method has been investigated. This experimental study was conducted on a laboratory scale. The effects of various operating parameters such as pH (3-11), LaFeO3 NPs dosage (0.1-1.0 g/L), contact time (15-120 min), temperature (303-318 K), and initial concentration of fluoride (15-40 mg/L) on fluoride adsorption were studied. The results showed that under optimal conditions of fluoride concentration of 20 mg/L, pH of 5, LaFeO3 NPs dosage of 0.9 g/L, temperature of 308 K, and contact time of 60 min, maximum percentage removal of 94.75 % was obtained. The process of fluoride adsorption on LaFeO3 NPs was found to depend on the Freundlich adsorption and Koble-Corrigan isotherm models. The monolayer adsorption capacity of LaFeO3 NPs was 2.575 mg/g. The kinetic data fitted best into the pseudo-second-order model considering the values of the regression coefficients (r2) and error functions used. The thermodynamics study indicated that the adsorption process was exothermic (ΔH°< 0) and spontaneous (ΔG°< 0) in nature. It could be concluded that the synthesized LaFeO3NPs can be used as an effective adsorbent for fluoride ions removal from aqueous solutions.