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Oubahmane M, Hdoufane I, Delaite C, Sayede A, Cherqaoui D, El Allali A. Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations. Pharmaceuticals (Basel) 2023; 16:ph16040608. [PMID: 37111365 PMCID: PMC10146715 DOI: 10.3390/ph16040608] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/08/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a serious global public health threat. The evolving strains of SARS-CoV-2 have reduced the effectiveness of vaccines. Therefore, antiviral drugs against SARS-CoV-2 are urgently needed. The main protease (Mpro) of SARS-CoV-2 is an extremely potent target due to its pivotal role in virus replication and low susceptibility to mutation. In the present study, a quantitative structure-activity relationship (QSAR) study was performed to design new molecules that might have higher inhibitory activity against SARS-CoV-2 Mpro. In this context, a set of 55 dihydrophenanthrene derivatives was used to build two 2D-QSAR models using the Monte Carlo optimization method and the Genetic Algorithm Multi-Linear Regression (GA-MLR) method. From the CORAL QSAR model outputs, the promoters responsible for the increase/decrease in inhibitory activity were extracted and interpreted. The promoters responsible for an increase in activity were added to the lead compound to design new molecules. The GA-MLR QSAR model was used to ensure the inhibitory activity of the designed molecules. For further validation, the designed molecules were subjected to molecular docking analysis and molecular dynamics simulations along with an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. The results of this study suggest that the newly designed molecules have the potential to be developed as effective drugs against SARS-CoV-2.
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Affiliation(s)
- Mehdi Oubahmane
- Laboratory of Molecular Chemistry, Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco
| | - Ismail Hdoufane
- Laboratory of Molecular Chemistry, Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco
| | - Christelle Delaite
- Laboratoire de Photochimie et d'Ingénierie Macromoléculaires (LPIM), Ecole Nationale Supérieure de Chimie de Mulhouse, Université de Haute-Alsace, 68100 Mulhouse, France
| | - Adlane Sayede
- University Artois, CNRS, Centrale Lille, University Lille, UMR 8181, Unité de Catalyse et Chimie du Solide (UCCS), F-62300 Lens, France
| | - Driss Cherqaoui
- Laboratory of Molecular Chemistry, Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco
| | - Achraf El Allali
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
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Goyal S, Rani P, Chahar M, Hussain K, Kumar P, Sindhu J. Quantitative structure activity relationship studies of androgen receptor binding affinity of endocrine disruptor chemicals with index of ideality of correlation, their molecular docking, molecular dynamics and ADME studies. J Biomol Struct Dyn 2023; 41:13616-13631. [PMID: 37010991 DOI: 10.1080/07391102.2023.2193991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 02/03/2023] [Indexed: 04/04/2023]
Abstract
Endocrine disrupter chemicals (EDCs) are both natural and man-made chemicals that mimic, block or interfere with human hormonal system. In the present manuscript, QSAR modeling was performed for the androgen disruptors that interfere with biosynthesis, metabolism or action of androgens that causes adverse effects on male reproductive system. A set of 96 EDCs that exhibited affinity towards androgen receptors (Log RBA) in rats were employed for carrying out QSAR studies using Hybrid descriptors (combination of HFG and SMILES) through Monte Carlo Optimization. Using index of ideality of correlation (TF2), five splits were formed and predictability of five models resulting from these splits was assessed by various validation parameters. Models resulted from first split was the top most one with R2validation = 0.7878. Structural attributes responsible for change in endpoint were studied by employing correlation weights of structural attributes. In order to further validate the model, new EDCs were designed using these attributes. In silico molecular modelling studies were performed to assess the detailed interactions with the receptor. The binding energies of all the designed compounds were observed to be better than lead and are in the range of -10.46 to -14.80. Molecular dynamics simulation of 100 ns was performed for ED01 and NED05. The results revealed that the protein-ligand complex bearing NED05 was more stable than lead ED01 exhibiting better interactions with the receptor. Further, in an attempt to assess their metabolism, ADME studies were evaluated using SwissADME. The developed model enables to predict the characteristics of designed compounds in an authentic way.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Surbhi Goyal
- Department of Chemistry, Baba Mastnath University, Rohtak, India
| | - Payal Rani
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - Monika Chahar
- Department of Chemistry, Baba Mastnath University, Rohtak, India
| | - Khalid Hussain
- Department of AS&H, Mewat Engineering College, Palla, Nuh, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
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Banjare P, Singh J, Papa E, Roy PP. Aquatic toxicity prediction of diverse pesticides on two algal species using QSTR modeling approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:10599-10612. [PMID: 36083366 DOI: 10.1007/s11356-022-22635-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
With the aim of identification of toxic nature of the diverse pesticides on the aquatic compartment, a large dataset of pesticides (n = 325) with experimental toxicity data on two algal test species (Pseudokirchneriella subcapitata (PS) (synonym: Raphidocelis subcapitata, Selenastrum capricornutum) and Scenedemus subspicatus (SS)) was gathered and subjected to quantitative structure toxicity relationship (QSTR) analysis to predict aquatic toxicity of pesticides. The QSTR models were developed by multiple linear regressions (MLRs), and the genetic algorithm (GA) was used for the variable selection. The developed GA-MLR models were statistically robust enough internally (Q2LOO = 0.620-0.663) and externally (Q2Fn = 0.693-0.868, CCCext = 0.843-0.877). The leverage approach of applicability domain (AD) and prediction reliability indicator assured the reliability of the developed models. The mechanistic interpretation highlighted that the presence of SO2, F and aromatic rings influenced the toxicity of pesticides towards PS species while the presence of alkyl, alkyl halide, aromatic rings and carbonyl was responsible for the toxicity of pesticides towards SS species. Additionally, we have reported the application of developed models to pesticides without experimental value and the cumulative toxicity of pesticides on the aquatic environment by using principal component analysis (PCA). The reliable prediction and prioritization of toxic compounds from the developed models will be useful in the aquatic toxicity assessment of pesticides.
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Affiliation(s)
- Purusottam Banjare
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Jagadish Singh
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India
| | - Ester Papa
- Department of Theoretical and Applied Sciences (DiSTA), University of Insubria, Via J.H. Dunant 3, 21100, Varese, Italy
| | - Partha Pratim Roy
- Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, 495009, India.
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Liman W, Oubahmane M, Hdoufane I, Bjij I, Villemin D, Daoud R, Cherqaoui D, El Allali A. Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus. Molecules 2022; 27:molecules27092729. [PMID: 35566079 PMCID: PMC9099611 DOI: 10.3390/molecules27092729] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/13/2022] [Accepted: 04/21/2022] [Indexed: 11/16/2022] Open
Abstract
Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R2 = 0.991 and Q2 = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R2 = 0.915 and Q2 = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.
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Affiliation(s)
- Wissal Liman
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco; (W.L.); (R.D.)
| | - Mehdi Oubahmane
- Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco; (M.O.); (I.H.); (D.C.)
| | - Ismail Hdoufane
- Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco; (M.O.); (I.H.); (D.C.)
| | - Imane Bjij
- Institut Supérieur des Professions Infirmières et Techniques de Santé (ISPITS), Dakhla 73000, Morocco;
| | - Didier Villemin
- Ecole Nationale Supérieure d’Ingénieurs (ENSICAEN) Laboratoire de Chimie Moléculaire et Thioorganique, UMR 6507 CNRS, INC3M, FR3038, Labex EMC3, Labex SynOrg ENSICAEN & Université de Caen, 14118 Caen, France;
| | - Rachid Daoud
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco; (W.L.); (R.D.)
| | - Driss Cherqaoui
- Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco; (M.O.); (I.H.); (D.C.)
| | - Achraf El Allali
- African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco; (W.L.); (R.D.)
- Correspondence:
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Lotfi S, Ahmadi S, Kumar P. Ecotoxicological prediction of organic chemicals toward Pseudokirchneriella subcapitata by Monte Carlo approach. RSC Adv 2022; 12:24988-24997. [PMID: 36199875 PMCID: PMC9434604 DOI: 10.1039/d2ra03936b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 08/19/2022] [Indexed: 11/21/2022] Open
Abstract
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…(…
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… 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. In the ecotoxicological risk assessment, acute toxicity is one of the most significant criteria.![]()
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Affiliation(s)
- Shahram Lotfi
- Department of Chemistry, Payame Noor University (PNU), 19395-4697 Tehran, Iran
| | - Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
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Lotfi S, Ahmadi S, Kumar P. The Monte Carlo approach to model and predict the melting point of imidazolium ionic liquids using hybrid optimal descriptors. RSC Adv 2021; 11:33849-33857. [PMID: 35497322 PMCID: PMC9042335 DOI: 10.1039/d1ra06861j] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 10/11/2021] [Indexed: 12/17/2022] Open
Abstract
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 (Tm). 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 Tm 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 RValidation2, QValidation2, 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 Tm are also extracted. 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.![]()
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Affiliation(s)
- Shahram Lotfi
- Department of Chemistry, Payame Noor University (PNU) 19395-4697 Tehran Iran
| | - Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University Tehran Iran
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University Kurukshetra Haryana 136119 India
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Exploring biological efficacy of novel benzothiazole linked 2,5-disubstituted-1,3,4-oxadiazole hybrids as efficient α-amylase inhibitors: Synthesis, characterization, inhibition, molecular docking, molecular dynamics and Monte Carlo based QSAR studies. Comput Biol Med 2021; 138:104876. [PMID: 34598068 DOI: 10.1016/j.compbiomed.2021.104876] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/14/2021] [Accepted: 09/14/2021] [Indexed: 12/29/2022]
Abstract
In an effort to explore a class of novel antidiabetic agents, we have made an effort to synergize the α-amylase inhibitory potential of 1,3-benzothiazole and 1,3,4-oxadiazole scaffolds by combining the two into a single structure via an ether linkage. The structure of synthesized benzothiazole clubbed oxadiazole derivatives are established by different spectral techniques. The synthesized hybrids are evaluated for their in vitro inhibitory potential against α-amylase. Compound 8f is found to be the most potent with a significant inhibition (87.5 ± 0.74% at 50 μg/mL, 82.27 ± 1.85% at 25 μg/mL and 79.94 ± 1.88% at 12.5 μg/mL) when compared to positive control acarbose (77.96 ± 2.06%, 71.17 ± 0.60%, 67.24 ± 1.16% at 50 μg/mL, 25 μg/mL and 12.5 μg/mL concentration). Molecular docking of the most potent enzyme inhibitor, 8f, shows promising interaction with the binding site of biological macromolecule Aspergillus oryzae α-amylase (PDB ID: 7TAA) and human pancreatic α-amylase (PDB ID: 3BAJ). To a step further, in-depth QSAR studies show a significant correlation between the experimental and the predicted inhibitory activities with the best Rvalidation2= 0.8701. The developed QSAR model can provide ample information about the structural features responsible for the increase and decrease of inhibitory activity. The mechanistic interpretation of the structure-activity relationship (SAR) is done with the help of combined computational calculations i.e. molecular docking and QSAR. Finally, molecular dynamic simulations are performed to get an insight into the binding mode of the most potent derivative with α-amylase from A. oryzae (PDB ID: 7TAA) and human pancreas (PDB ID: 3BAJ).
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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 AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:817-834. [PMID: 34530657 DOI: 10.1080/1062936x.2021.1973095] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [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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Duhan M, Sindhu J, Kumar P, Devi M, Singh R, Kumar R, Lal S, Kumar A, Kumar S, Hussain K. Quantitative structure activity relationship studies of novel hydrazone derivatives as α-amylase inhibitors with index of ideality of correlation. J Biomol Struct Dyn 2020; 40:4933-4953. [PMID: 33357037 DOI: 10.1080/07391102.2020.1863861] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The present manuscript describes the synthesis, α-amylase inhibition, in silico studies and in-depth quantitative structure-activity relationship (QSAR) of a library of aroyl hydrazones based on benzothiazole skeleton. All the compounds of the developed library are characterized by various spectral techniques. α-Amylase inhibitory potential of all compounds has been explored, where compound 7n exhibits remarkable α-amylase inhibition of 87.5% at 50 µg/mL. Robust QSAR models are made by using the balance of correlation method in CORAL software. The chemical structures at different concentration with optimal descriptors are represented by SMILES. A data set of 66 SMILES of 22 hydrazones at three distinct concentrations are prepared. The significance of the index of ideality of correlation (IIC) with applicability domain (AD) is also studied at depth. A QSAR model with best Rvalidation2 = 0.8587 for split 1 is considered as a leading model. The outliers and promoters of increase and decrease of endpoint are also extracted. The binding modes of the most active compound, that is, 7n in the active site of Aspergillus oryzae α-amylase (PDB ID: 7TAA) are also explored by in silico molecular docking studies. Compound 7n displays high resemblance in binding mode and pose with the standard drug acarbose. Molecular dynamics simulations performed on protein-ligand complex for 100 ns, the protein gets stabilised after 20 ns and remained below 2 Å for the remaining simulation. Moreover, the deviation observed in RMSF during simulation for each amino acid residue with respect to Cα carbon atom is insignificant.
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Affiliation(s)
- Meenakshi Duhan
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ramesh Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambeshwar University of Science and Technology, Hisar, India
| | - Sudhir Kumar
- Department of MBB&B, COBS&H, CCS Haryana Agricultural University, Hisar, India
| | - Khalid Hussain
- Department of Applied Sciences and Humanities, Mewat Engineering College, Nuh, India
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Quantitative structure toxicity analysis of ionic liquids toward acetylcholinesterase enzyme using novel QSTR models with index of ideality of correlation and correlation contradiction index. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.114055] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Toropov AA, Toropova AP. Correlation intensity index: Building up models for mutagenicity of silver nanoparticles. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 737:139720. [PMID: 32554036 DOI: 10.1016/j.scitotenv.2020.139720] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/21/2020] [Accepted: 05/24/2020] [Indexed: 06/11/2023]
Abstract
Nanomaterials become significant component of economics. Consequently, nanomaterials become object of environmental sciences. There is a traditional list of endpoints which are indicators of the ecological risk. Mutagenicity is one of important component in this list. The quasi-SMILES approach, that in contrast to majority of work dedicated to modelling behaviour of nanomaterials gives possibility to consider experimental conditions as well as other circumstances which can impact the behaviour of nanomaterials is suggested. This is carried out via so-called quasi-SMILES. The quasi-SMILES is a line on of codes that contains all the above available eclectic data. Modelling process aimed to build up a model involves Correlation Intensity Index (CII) that is a new criterion of predictive potential of models. The scheme of calculation of CII is described in this work in the first time. The applying of CII together with Index of Ideality Correlation (IIC) in modelling of mutagenicity of silver nanoparticles by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral) indicates that application of the CII improves the predictive potential of these models for three random splits into the training set (75%) and validation set (25%).
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Affiliation(s)
- Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
| | - Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy.
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Kumar A, Kumar P. Identification of good and bad fragments of tricyclic triazinone analogues as potential PKC-θ inhibitors through SMILES–based QSAR and molecular docking. Struct Chem 2020. [DOI: 10.1007/s11224-020-01629-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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13
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Jafari K, Fatemi MH, Toropova AP, Toropov AA. Correlation Intensity Index (CII) as a criterion of predictive potential: Applying to model thermal conductivity of metal oxide-based ethylene glycol nanofluids. Chem Phys Lett 2020. [DOI: 10.1016/j.cplett.2020.137614] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Kumar A, Sindhu J, Kumar P. In-silico identification of fingerprint of pyrazolyl sulfonamide responsible for inhibition of N-myristoyltransferase using Monte Carlo method with index of ideality of correlation. J Biomol Struct Dyn 2020; 39:5014-5025. [DOI: 10.1080/07391102.2020.1784286] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambeshwar University of Science and Technology, Hisar, Haryana, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, Haryana, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
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Bagri K, Kumar A, Nimbhal M, Kumar P. Index of ideality of correlation and correlation contradiction index: a confluent perusal on acetylcholinesterase inhibitors. MOLECULAR SIMULATION 2020. [DOI: 10.1080/08927022.2020.1770753] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Kiran Bagri
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar, India
| | - Manisha Nimbhal
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science & Technology, Hisar, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
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16
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Duhan M, Singh R, Devi M, Sindhu J, Bhatia R, Kumar A, Kumar P. Synthesis, molecular docking and QSAR study of thiazole clubbed pyrazole hybrid as α-amylase inhibitor. J Biomol Struct Dyn 2019; 39:91-107. [DOI: 10.1080/07391102.2019.1704885] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Meenakshi Duhan
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, Haryana, India
| | - Rimpy Bhatia
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambeshwar University of Science and Technology, Hisar, Haryana, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
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17
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The index of ideality of correlation: A statistical yardstick for better QSAR modeling of glucokinase activators. Struct Chem 2019. [DOI: 10.1007/s11224-019-01468-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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18
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Kumar P, Kumar A, Sindhu J. In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:525-541. [PMID: 31331203 DOI: 10.1080/1062936x.2019.1629998] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 06/06/2019] [Indexed: 06/10/2023]
Abstract
Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II diabetes can be checked by the inhibition of DGAT1 enzyme. Quantitative structure-activity relationship (QSAR) modelling is an essential technique in drug design and development. To study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGAT1 inhibitors. QSAR models were derived by using the optimal descriptor based on SMILES notation. Different statistical parameters including the novel index of ideality of correlation were applied to validate the generated QSAR models. Four random splits were prepared from the data set. The statistical criteria r2 = 0.8129, CCC = 0.8979 and Q2 = 0.7962 of the validation set of split 1 were the best; therefore, the developed QSAR model of split 1 was decided to be the leading model. The molecular fragments, which were promoter of endpoint increase or decrease were also determined. Thirteen new DGAT1 inhibitors were designed from the lead compound DGAT011.
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Affiliation(s)
- P Kumar
- Department of Chemistry, Kurukshetra University , Kurukshetra , India
| | - A Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology , Hisar , India
| | - J Sindhu
- Department of Chemsitry, COBS&H CCS Haryana Agriculture University , Hisar , India
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