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Singh R, Kumar P, Sindhu J, Kumar A, Lal S. CORAL: probing the structural requirements for α-amylase inhibition activity of 5-(3-arylallylidene)-2-(arylimino)thiazolidin-4-one derivatives based on QSAR with correlation intensity index, molecular docking, molecular dynamics, and ADMET studies. J Biomol Struct Dyn 2023:1-18. [PMID: 37815000 DOI: 10.1080/07391102.2023.2265490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
The present study aims to examine the structural requirements governing α-amylase inhibitory activity of 5-(3-arylallylidene)-2-(arylimino)thiazolidin-4-one derivatives and their precursors by employing a multifaceted approach combining in vitro and in silico studies. The in vitro assay findings revealed strong inhibitory effect of this class of compounds against α-amylase and compound 20 exhibited maximum percentage inhibition of 88.54 ± 0.69, 84.98 ± 0.40, 77.26 ± 0.75, 67.80 ± 0.54, and 62.93 ± 1.17 at 200, 100, 50, 25, and 12.5 µg mL-1, respectively. Multiple CORAL QSAR models were developed from the randomly distributed eight splits by employing two target functions (TF1, TF2 with WCII = 0.0 and = 0.3, respectively), and the quality of predictions by the produced models was validated with the help of various statistical parameters. The model M-4 (R2Val = 0.8799) and model M-11 (R2Val = 0.9064) were the leading models developed by using TF1 and TF2. We designed five new congeneric inhibitors (D-1 to D-5) by incorporating SMILES features positively correlating with the activity. Molecular docking experiments were carried out to confirm the binding of these new inhibitors with the biological receptor α-amylase (PDB ID: 7TAA). Furthermore, molecular dynamic simulations provided a thorough knowledge of the binding process by shedding insight into the dynamic behavior and stability of the ligand-receptor complex over time. The results of this study highlight the key structural characteristics needed for improved α-amylase inhibitory efficacy and provide a rational basis for the development of more effective inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
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Kumar P, Singh R, Kumar A, Toropova AP, Toropov AA, Devi M, Lal S, Sindhu J, Singh D. Identifications of good and bad structural fragments of hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids with correlation intensity index and consensus modelling using Monte Carlo based QSAR studies, their molecular docking and ADME analysis. SAR QSAR Environ Res 2022; 33:677-700. [PMID: 36093620 DOI: 10.1080/1062936x.2022.2120068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
The application of QSAR along with other in silico tools like molecular docking, and molecular dynamics provide a lot of promise for finding new treatments for life-threatening diseases like Type 2 diabetes mellitus (T2DM). The present study is an attempt to develop Monte Carlo algorithm-based QSAR models using freely available CORAL software. The experimental data on the α-amylase inhibition by a series of benzothiazole-linked hydrazone/2,5-disubstituted-1,3,4-oxadiazole hybrids were selected as endpoint for the model generation. Initially, a total of eight QSAR models were built using correlation intensity index (CII) as a criterion of predictive potential. The model developed from split 6 using CII was the most reliable because of the highest numerical value of the determination coefficient of the validation set (r2VAL = 0.8739). The important structural fragments responsible for altering the endpoint were also extracted from the best-built model. With the goal of improved prediction quality and lower prediction errors, the validated models were used to build consensus models. Molecular docking was used to know the binding mode and pose of the selected derivatives. Further, to get insight into their metabolism by living beings, ADME studies were investigated using internet freeware, SwissADME.
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Affiliation(s)
- P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - R Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - A Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, India
| | - A P Toropova
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - A A Toropov
- Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - M Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - S Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - J Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - D Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, India
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Kumar P, Kumar A, Singh D. CORAL: Development of a hybrid descriptor based QSTR model to predict the toxicity of dioxins and dioxin-like compounds with correlation intensity index and consensus modelling. Environ Toxicol Pharmacol 2022; 93:103893. [PMID: 35654373 DOI: 10.1016/j.etap.2022.103893] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/21/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
In the present study, ninety-five halogenated dioxins and related chemicals (dibenzo-p-dioxins, dibenzofurans, biphenyls, and naphthalene) with endpoint pEC50 were used to develop twelve quantitative structure toxicity relationship (QSTR) models using inbuilt Monte Carlo algorithm of CORAL software. The hybrid optimal descriptor of correlation weights (DCW) using a combination of SMILES and HSG (hydrogen suppressed graph) was employed to generate QSTR models. Three target functions i.e. TF1 (WIIC=WCII=0), TF2 (WIIC= 0.3 & WCII=0) and TF3 (WIIC= 0.0 &WCII=0.3) were employed to develop robust QSTR models and the statistical outcomes of each target function were compared with each other. The correlation intensity index (CII) was found a reliable benchmark of the predictive potential for QSTR models. The numerical value of the determination coefficient of the validation set of split 1 computed by TF3 was found highest (RValid2=0.8438). The fragments responsible for the toxicity of dioxins and related chemicals were also identified in terms of the promoter of increase/decrease for pEC50. Three random splits (Split 1, Split 2 and Split 4) were selected for the extraction of the promoter of increase/decrease for pEC50. In the last, consensus modelling was performed using the intelligent consensus tool of DTC lab (https://dtclab.webs.com/software-tools). The original consensus model, which was created by combining four distinct models employing the split 4 arrangement, was more predictive for the validation set and the numerical value of the determination coefficient of the test set (validation set) was increased from 0.8133 to 0.9725. For the validation set of split 4, the mean absolute error (MAE 100%) was also lowered from 0.513 to 0.2739.
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Affiliation(s)
- Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana 136119, India.
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, Haryana 125001, India.
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, Haryana 124001, India
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Abstract
VEGAHUB (www.vegahub.eu) is a repository of freely available, downloadable tools based on computational toxicology methodologies. The main software tool available in VEGAHUB is VEGA QSAR software encoding more than 90 quantitative structure-activity relationship (QSAR) models for tens of endpoints for human toxicology, ecotoxicology, environmental, physico-chemical and toxicokinetic properties. However, beyond VEGA QSAR, VEGAHUB offers several other tools. Here, we present these resources, the possibilities to fully exploit them and the ways in which to integrate results provided by different VEGAHUB tools. Read-across and weight-of-evidence represent a major advantage of VEGAHUB. Integration between hazard and exposure is provided within innovative tools, which are specific for well-defined scenarios, such as those for cosmetic products. Prioritisation can be achieved by integrating results from 48 models. Finally, we highlight how some tools may not only fit predefined endpoints but also could be applied to general problems and research applications in the QSAR field. A couple of examples are provided, in which a critical assessment of the predictions and the documentation associated with the prediction are considered, in order to properly assess the quality of the results. These results may be associated with different levels of uncertainty or even be conflicting.
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Affiliation(s)
| | - Anna Lombardo
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, 9361Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.,This article is part of the Virtual Special Collection on In Silico Tools
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, 9361Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
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Kumar A, Kumar P. Prediction of power conversion efficiency of phenothiazine-based dye-sensitized solar cells using Monte Carlo method with index of ideality of correlation. SAR QSAR Environ Res 2021; 32:817-834. [PMID: 34530657 DOI: 10.1080/1062936x.2021.1973095] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
Simplified molecular-input line-entry system (SMILES) notation and inbuilt Monte Carlo algorithm of CORAL software were employed to construct generative and prediction QSPR models for the analysis of the power conversion efficiency (PCE) of 215 phenothiazine derivatives. The dataset was divided into four splits and each split was further divided into four sets. A hybrid descriptor, a combination of SMILES and hydrogen suppressed graph (HSG), was employed to build reliable and robust QSPR models. The role of the index of ideality of correlation (IIC) was also studied in depth. We performed a comparative study to predict PCE using two target functions (TF1 without IIC and TF2 with IIC). Eight QSPR models were developed and the models developed with TF2 was shown robust and reliable. The QSPR model generated from split 4 was considered a leading model. The different statistical benchmarks were computed for the lead model and these were rtraining set2=0.7784; rinvisible training set2=0.7955; rcalibration set2=0.7738; rvalidation set2=0.7506; Qtraining set2=0.7691; Qinvisible training set2=0.7850; Qcalibration set2=0.7501; Qvalidation set2=0.7085; IICtraining set = 0.8590; IICinvisible training set = 0.8297; IICcalibration set = 0.8796; IICvalidation set = 0.8293, etc. The promoters of increase and decrease of endpoint PCE were also extracted.
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Affiliation(s)
- A Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
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Ahmadi S, Lotfi S, Kumar P. A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants. SAR QSAR Environ Res 2020; 31:935-950. [PMID: 33179988 DOI: 10.1080/1062936x.2020.1842495] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 10/22/2020] [Indexed: 06/11/2023]
Abstract
The Monte Carlo algorithm was applied to formulate a robust quantitative structure-property relationship (QSPR) model to compute the reactions rate constants of hydrated electron values for a data set of 309 water contaminants containing 125 aliphatic and 184 phenyl-based chemicals. The QSPR models were computed with the hybrid optimal descriptors which were procured by combining the SMILES and hydrogen-suppressed molecular graph for both classes of compounds. Approximately 75% of the total experimental data set was randomly divided into training and invisible training sets, while approximately 25% was divided into calibration and validation sets. The authenticity and robustness of the developed QSPR models were also judged by the Index of Ideality of Correlation. In QSPR modelling of aliphatic compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.852-0.905, 0.815-0.894, 0.839-0.897 and 0.737-0.867, respectively. Whereas, in the QSPR modelling of phenyl-based compounds, the numerical values of r T r a i n i n g 2 , r V a l i d a t i o n 2 , Q T r a i n i n g 2 and Q V a l i d a t i o n 2 were in the range of 0.867-0.896, 0.852-0.865, 0.816-0.850 and 0.760-0.762, respectively. The structural attributes, which are promoters of l o g K e a q - increase/decrease are also extracted from the SMILES notation for mechanistic interpretation. These QSPR models can also be applied to compute the reaction rate constants of organic contaminants.
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Affiliation(s)
- S Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University , Tehran, Iran
| | - S Lotfi
- Department of Chemistry, Payame Noor University (PNU) , Tehran, Iran
| | - P Kumar
- Department of Chemistry, Kurukshetra University , Kurukshetra, Haryana, India
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Jain S, Amin SA, Adhikari N, Jha T, Gayen S. Good and bad molecular fingerprints for human rhinovirus 3C protease inhibition: identification, validation, and application in designing of new inhibitors through Monte Carlo-based QSAR study. J Biomol Struct Dyn 2020; 38:66-77. [PMID: 30646829 DOI: 10.1080/07391102.2019.1566093] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 01/01/2019] [Indexed: 01/12/2023]
Abstract
HRV 3 C protease (HRV 3Cpro) is an important target for common cold and upper respiratory tract infection. Keeping in view of the non-availability of drug for the treatment, newer computer-based modelling strategies should be applied to rationalize the process of antiviral drug discovery in order to decrease the valuable time and huge expenditure of the process. The present work demonstrates a structure wise optimization using Monte Carlo-based QSAR method that decomposes ligand compounds (in SMILES format) into several molecular fingerprints/descriptors. The current state-of-the-art in QSAR study involves the balance of correlation approach using four different sets: training, invisible training, calibration, and validation. The final models were also validated through mean absolute error, index of ideality of correlation, Y-randomization and applicability domain analysis. R2 and Q2 values for the best model were 0.8602, 0.8507 (training); 0.8435, 0.8331 (invisible training); 0.7424, 0.7020 (calibration); 0.5993, 0.5216 (validation), respectively. The process identified some molecular substructures as good and bad fingerprints depending on their effect to increase or decrease the HRV 3Cpro inhibition. Finally, new inhibitors were designed based on the fundamental concept to replace the bad fragments with the good fragments as well as including more good fragments into the structure. The study points out the importance of the fingerprint based drug design strategy through Monte Carlo optimization method in the modelling of HRV 3Cpro inhibitors.
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Affiliation(s)
- Sanskar Jain
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. Harisingh Gour University (A Central University), Sagar, India
| | - Sk Abdul Amin
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Nilanjan Adhikari
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Tarun Jha
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. Harisingh Gour University (A Central University), Sagar, India
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Chauhan S, Kumar P, Kumar A. Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method. SAR QSAR Environ Res 2019; 30:145-159. [PMID: 30777782 DOI: 10.1080/1062936x.2019.1568299] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Indexed: 06/09/2023]
Abstract
Fructose-1,6-bisphosphatase (FBPase) is an enzyme important for regulation of gluconeogenesis, which is a major process in the liver responsible for glucose production. Inhibition of FBPase enzyme causing blockage of the gluconeogenesis process represents a newer scheme in the progress of anti-diabetic drugs. The current research describes the development of hybrid optimal descriptors-based quantitative structure-activity relationship (QSAR) models intended for a set of 62 FBPase inhibitors with the Monte Carlo method. The molecular structures were expressed by the simplified molecular input line entry system (SMILES) notation. Three splits were prepared by random division of the molecules into training set, calibration set and validation set. Statistical parameters obtained from QSAR modelling were good for various designed splits. The best QSAR model showed the following parameters: the values of r2 for calibration set and validation set of the best model were 0.6837 and 0.8623 and of Q2 were 0.6114 and 0.8036, respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoter of the endpoint. Further, these structural attributes were used in designing of new FBPase inhibitors and a molecular docking study was completed for the determination of interactions of the designed molecules with the enzyme.
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Affiliation(s)
- S Chauhan
- a Department of Pharmaceutical Sciences , Guru Jambheshwar University of Science & Technology , Hisar , India
| | - P Kumar
- b Department of Chemistry , Kurukshetra University , Kurukshetra , India
| | - A Kumar
- a Department of Pharmaceutical Sciences , Guru Jambheshwar University of Science & Technology , Hisar , India
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Kumar P, Kumar A, Sindhu J. Design and development of novel focal adhesion kinase (FAK) inhibitors using Monte Carlo method with index of ideality of correlation to validate QSAR. SAR QSAR Environ Res 2019; 30:63-80. [PMID: 30793981 DOI: 10.1080/1062936x.2018.1564067] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 12/24/2018] [Indexed: 06/09/2023]
Abstract
Quantitative structure-activity relationship (QSAR) modelling of 55 focal adhesion kinase (FAK) (EC 2.7.10.2) inhibitors of triazinic nature was performed using the Monte Carlo method. The QSAR models were designed by CORAL software, and optimal descriptors were calculated with the simplified molecular input line entry system (SMILES). Four splits were made from the triazinic derivative data by random division into training, invisible training, calibration and validation sets. The QSAR results from these four random splits were robust, very simple, predictive and reliable. The best statistical parameters of the validation set (r2 = 0.8398 and Q2 = 0.7722) for the QSAR equation for split 3 with IIC = 0.9127 were obtained. The predictive potential of QSAR models of FAK inhibitors was explored by applying the index of ideality of correlation (IIC), which is a new criterion for the prediction of the potential for quantitative structure-property activity relationships (QSPRs/QSARs). The present method follows OECD principles.
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Affiliation(s)
- P Kumar
- a Department of Chemistry , Kurukshetra University , Kurukshetra , Haryana , India
| | - A Kumar
- b Department of Pharmaceutical Sciences , Guru Jambheshwar University of Science and Technology , Hisar , Haryana , India
| | - J Sindhu
- c K. M. Govt. College , Narwana , Haryana , India
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Ahmadi S, Akbari A. Prediction of the adsorption coefficients of some aromatic compounds on multi-wall carbon nanotubes by the Monte Carlo method. SAR QSAR Environ Res 2018; 29:895-909. [PMID: 30332923 DOI: 10.1080/1062936x.2018.1526821] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 09/18/2018] [Indexed: 06/08/2023]
Abstract
In this investigation, quantitative structure-property relationship (QSPR) modelling of adsorption coefficients of 69 aromatic compounds on multi-wall carbon nanotubes (MWCNTs) was studied using the Monte Carlo method. QSPR models were calculated with CORAL software, and optimal descriptors were calculated with the simplified molecular input line entry system (SMILES) and hydrogen-suppressed molecular graphs (HSGs). The aromatic compound data set was randomly split into training, invisible training, calibration and validation sets. Analysis of three probes of the Monte Carlo optimization with three random splits was done. The results from three random splits displayed robust, very simple, predictable and reliable models for the training, invisible training, calibration and validation sets with a coefficient of determination (r2) equal to 0.9463-0.8528, 0.9020-0.8324, 0.9606-0.9178 and 0.9573-0.8228, respectively. As a result, the models obtained help to identify the hybrid descriptors for the increase and the decrease of the adsorption coefficient of aromatic compounds on MWCNTs. This simple QSPR model can be used for the prediction of the adsorption coefficient of numerous aromatic compounds on MWCNTs.
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Affiliation(s)
- S Ahmadi
- a Department of Chemistry , Kermanshah Branch, Islamic Azad University , Kermanshah , Iran
| | - A Akbari
- a Department of Chemistry , Kermanshah Branch, Islamic Azad University , Kermanshah , Iran
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Toropov AA, Sizochenko N, Toropova AP, Leszczynski J. Towards the Development of Global Nano-Quantitative Structure-Property Relationship Models: Zeta Potentials of Metal Oxide Nanoparticles. Nanomaterials (Basel) 2018; 8:nano8040243. [PMID: 29662037 PMCID: PMC5923573 DOI: 10.3390/nano8040243] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 04/12/2018] [Accepted: 04/12/2018] [Indexed: 11/25/2022]
Abstract
Zeta potential indirectly reflects a charge of the surface of nanoparticles in solutions and could be used to represent the stability of the colloidal solution. As processes of synthesis, testing and evaluation of new nanomaterials are expensive and time-consuming, so it would be helpful to estimate an approximate range of properties for untested nanomaterials using computational modeling. We collected the largest dataset of zeta potential measurements of bare metal oxide nanoparticles in water (87 data points). The dataset was used to develop quantitative structure–property relationship (QSPR) models. Essential features of nanoparticles were represented using a modified simplified molecular input line entry system (SMILES). SMILES strings reflected the size-dependent behavior of zeta potentials, as the considered quasi-SMILES modification included information about both chemical composition and the size of the nanoparticles. Three mathematical models were generated using the Monte Carlo method, and their statistical quality was evaluated (R2 for the training set varied from 0.71 to 0.87; for the validation set, from 0.67 to 0.82; root mean square errors for both training and validation sets ranged from 11.3 to 17.2 mV). The developed models were analyzed and linked to aggregation effects in aqueous solutions.
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Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, 20156 Milano, Italy.
| | - Natalia Sizochenko
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS 39217, USA.
- Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, 20156 Milano, Italy.
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Jackson State University, Jackson, MS 39217, USA.
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Amata E, Marrazzo A, Dichiara M, Modica MN, Salerno L, Prezzavento O, Nastasi G, Rescifina A, Romeo G, Pittalà V. Comprehensive data on a 2D-QSAR model for Heme Oxygenase isoform 1 inhibitors. Data Brief 2017; 15:281-299. [PMID: 29034293 PMCID: PMC5635207 DOI: 10.1016/j.dib.2017.09.036] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 09/07/2017] [Accepted: 09/19/2017] [Indexed: 01/22/2023] Open
Abstract
The data have been obtained from the Heme Oxygenase Database (HemeOxDB) and refined according to the 2D-QSAR requirements. These data provide information about a set of more than 380 Heme Oxygenase-1 (HO-1) inhibitors. The development of the 2D-QSAR model has been undertaken with the use of CORAL software using SMILES, molecular graphs and hybrid descriptors (SMILES and graph together). The 2D-QSAR model regressions for HO-1 half maximal inhibitory concentration (IC50) expressed as pIC50 (pIC50=−LogIC50) are here included. The 2D-QSAR model was also employed to predict the HO-1 pIC50values of the FDA approved drugs that are herewith reported.
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Affiliation(s)
- Emanuele Amata
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Agostino Marrazzo
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Maria Dichiara
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Maria N Modica
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Loredana Salerno
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Orazio Prezzavento
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Giovanni Nastasi
- Department of Mathematics and Computer Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Antonio Rescifina
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Giuseppe Romeo
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Valeria Pittalà
- Department of Drug Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy
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Abstract
Traditional Quantitative Structure-Activity Relationships (QSAR) models based on molecular descriptors as translators of chemical information show some drawbacks in predicting toxicity of nanomaterials due to their unique properties and to their nonhomogeneous structure.This chapter provides instructions on how to use CORAL, freely available software for building nano-QSAR models. CORAL makes use of descriptors based on "quasi-SMILES" representing physicochemical features and/or experimental conditions as an alternative to traditional SMILES encoding chemical structure to build up predictive nano-QSAR models for cytotoxicity.
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Affiliation(s)
- Serena Manganelli
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Via La Masa 19, Milan, 20156, Italy.
| | - Emilio Benfenati
- Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche "Mario Negri", Via La Masa 19, Milan, 20156, Italy
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Manganelli S, Benfenati E, Manganaro A, Kulkarni S, Barton-Maclaren TS, Honma M. New Quantitative Structure-Activity Relationship Models Improve Predictability of Ames Mutagenicity for Aromatic Azo Compounds. Toxicol Sci 2016; 153:316-26. [PMID: 27413112 DOI: 10.1093/toxsci/kfw125] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Existing Quantitative Structure-Activity Relationship (QSAR) models have limited predictive capabilities for aromatic azo compounds. In this study, 2 new models were built to predict Ames mutagenicity of this class of compounds. The first one made use of descriptors based on simplified molecular input-line entry system (SMILES), calculated with the CORAL software. The second model was based on the k-nearest neighbors algorithm. The statistical quality of the predictions from single models was satisfactory. The performance further improved when the predictions from these models were combined. The prediction results from other QSAR models for mutagenicity were also evaluated. Most of the existing models were found to be good at finding toxic compounds but resulted in many false positive predictions. The 2 new models specific for this class of compounds avoid this problem thanks to a larger set of related compounds as training set and improved algorithms.
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Affiliation(s)
- Serena Manganelli
- *Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, via La Masa 19, Milano 20156, Italy
| | - Emilio Benfenati
- *Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, via La Masa 19, Milano 20156, Italy
| | - Alberto Manganaro
- *Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, via La Masa 19, Milano 20156, Italy
| | - Sunil Kulkarni
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, Ontario, Canada
| | | | - Masamitsu Honma
- Division of Genetics & Mutagenesis National Institute of Health Sciences 1-18-1 Kamiyoga, Setagaya-Ku, Tokyo 158-8501, Japan
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Abstract
Simplified molecular input line entry system (SMILES) notations of 116 π-conjugated organic compounds have been used in three random splits to develop single optimal descriptor based quantitative structure-property relationships (QSPR) models for the prediction of dielectric constants by CORAL (CORrelation And Logic). Four kinds of optimal descriptors were obtained based on SMILES, hydrogen suppressed graph (HSG), graph of atomic orbitals (GAO) and hybrid descriptors. The Monte Carlo optimization was carried out for each random split by three different methods: (i) classic scheme; (ii) balance of correlations; and (iii) balance of correlations with ideal slopes. The QSPR models gave reliable and accurate values of dielectric constant for all the π-conjugated organic compounds. SMILES and the hybrid-based QSPR model provided the best accuracy for the prediction of dielectric constants. Statistical characteristics of the QSPR model-1 based on classic scheme method are n = 110, r(2) = 0.860, Q(2) = 0.860, s = 1.84, MAE = 1.30 and F = 696 (training set), n = 6, r(2) = 0.947, Q(2) = 0.876, s = 0.955, MAE = 0.647 and F = 71 (test set). These QSPR models are further validated by an external validation set of 25 molecules and the robustness is checked by parameters like k, kk, rm(2), r(*)m(2), average rm(2), ∆rm(2)and randomization technique ([Formula: see text]).
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Affiliation(s)
- P G R Achary
- a Department of Chemistry , Institute of Technical Education and Research (ITER), Siksha 'O' Anusandhan University , Bhubaneswar , Odisha , India
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