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Azimi A, Ahmadi S, Kumar A, Qomi M, Almasirad A. SMILES-Based QSAR and Molecular Docking Study of Oseltamivir Derivatives as Influenza Inhibitors. Polycycl Aromat Compd 2022. [DOI: 10.1080/10406638.2022.2067194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Atena Azimi
- Faculty of Pharmacy, Tehran Medical Sciences, Department of Medicinal Chemistry, Islamic Azad University, Tehran, Iran
| | - Shahin Ahmadi
- Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Department of Chemistry, Islamic Azad University, Tehran, Iran
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Mahnaz Qomi
- Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Department of Chemistry, Islamic Azad University, Tehran, Iran
- Active Pharmaceutical Ingredients Research (APIRC), Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Ali Almasirad
- Faculty of Pharmacy, Tehran Medical Sciences, Department of Medicinal Chemistry, Islamic Azad University, Tehran, Iran
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Ahmadi S, Moradi Z, Kumar A, Almasirad A. SMILES-based QSAR and molecular docking study of xanthone derivatives as α-glucosidase inhibitors. J Recept Signal Transduct Res 2021; 42:361-372. [PMID: 34384326 DOI: 10.1080/10799893.2021.1957932] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
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.
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Affiliation(s)
- Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Zohreh Moradi
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Ali Almasirad
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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Ghiasi T, Ahmadi S, Ahmadi E, Talei Bavil Olyai MR, Khodadadi Z. The index of ideality of correlation: QSAR studies of hepatitis C virus NS3/4A protease inhibitors using SMILES descriptors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:495-520. [PMID: 34074200 DOI: 10.1080/1062936x.2021.1925344] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 04/29/2021] [Indexed: 06/12/2023]
Abstract
Robust and reliable QSAR models were developed to predict half-maximal inhibitory concentration (IC50) values of hepatitis C virus NS3/4A protease inhibitors from the Monte Carlo technique. 524 HCV NS3/4A protease inhibitors were extracted from the scientific literature to create a reasonably large set. The models were developed using CORAL software by using two target functions namely target function 1 (TF1) without applying the index of ideality of correlation (IIC) and target function 2 (TF2) that uses IIC. The constructed models based on TF2 were statistically more significant and robust than the models based on TF1. The determination coefficients (r2) of training and test sets were 0.86 and 0.88 for the best split based on TF2. The promoters of the increase/decrease of activity were also extracted and interpreted in detail. The model interpretation results explain the role of different structural attributes in predicting the pIC50 values of hepatitis C virus NS3/4A protease inhibitors. Based on the mechanistic model interpretation results, eight new compounds were designed and their pIC50 values were predicted based on the average prediction of ten models.
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Affiliation(s)
- T Ghiasi
- Department of Chemistry, Faculty of Science, Islamic Azad University, South Tehran Branch, Tehran, Iran
| | - S Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - E Ahmadi
- Department of Chemistry, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran
| | - M R Talei Bavil Olyai
- Department of Chemistry, Faculty of Science, Islamic Azad University, South Tehran Branch, Tehran, Iran
| | - Z Khodadadi
- Department of Chemistry, Faculty of Science, Islamic Azad University, South Tehran Branch, Tehran, Iran
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The predictive model for band gap prediction of metal oxide nanoparticles based on quasi-SMILES. Struct Chem 2021. [DOI: 10.1007/s11224-021-01748-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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5
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Javidfar M, Ahmadi S. QSAR modelling of larvicidal phytocompounds against Aedes aegypti using index of ideality of correlation. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:717-739. [PMID: 32930630 DOI: 10.1080/1062936x.2020.1806922] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 08/04/2020] [Indexed: 06/11/2023]
Abstract
Aedes aegypti is the primary vector of several infectious viruses that cause yellow, dengue, chikungunya, and Zika fevers. Recently, plant-derived products have been tested as safe and eco-friendly larvicides against Ae. aegypti. The present study aimed to improve QSAR models for 62 larvicidal phytocompounds against Ae. aegypti via the Monte Carlo method based on the index of the ideality of correlation (IIC) criterion. The representation of structures was done with SMILES. Three splits were prepared randomly and three QSAR models were constructed using IIC target function. The molecular descriptors were selected from SMILES descriptors and the hydrogen-filled molecular graphs. The predictability of three models was evaluated on the validation sets, the r 2 of which was 0.9770, 0.8660, and 0.8565 for models 1 to 3, respectively. The statistical results of three randomized splits indicated that robust, simple, predictive, and reliable models were obtained for different sets. From the modelling results, important descriptors were identified to enhance and reduce the larvicidal activity of compounds. Based on the identified important descriptors, some new structures of larvicidal compounds were proposed. The larvicidal activity of novel molecules designed further was supported by docking studies. Using the simple QSAR model, one can predict pLC50 of new similarity larvicidal phytocompounds.
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Affiliation(s)
- M Javidfar
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University , Tehran, Iran
| | - S Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University , Tehran, Iran
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Toropov AA, Toropova AP, Marzo M, Benfenati E. Use of the index of ideality of correlation to improve aquatic solubility model. J Mol Graph Model 2020; 96:107525. [DOI: 10.1016/j.jmgm.2019.107525] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 11/27/2019] [Accepted: 12/23/2019] [Indexed: 12/18/2022]
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7
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Predictive QSAR modeling for the antioxidant activity of natural compounds derivatives based on Monte Carlo method. Mol Divers 2020; 25:87-97. [PMID: 31933105 DOI: 10.1007/s11030-019-10026-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Accepted: 12/23/2019] [Indexed: 12/19/2022]
Abstract
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.
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Ahmadi S, Mehrabi M, Rezaei S, Mardafkan N. Structure-activity relationship of the radical scavenging activities of some natural antioxidants based on the graph of atomic orbitals. J Mol Struct 2019. [DOI: 10.1016/j.molstruc.2019.04.103] [Citation(s) in RCA: 15] [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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Solov'ev V, Tsivadze A, Marcou G, Varnek A. Classification of Metal Binders by Naïve Bayes Classifier on the Base of Molecular Fragment Descriptors and Ensemble Modeling. Mol Inform 2019; 38:e1900002. [DOI: 10.1002/minf.201900002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 03/15/2019] [Indexed: 12/31/2022]
Affiliation(s)
- Vitaly Solov'ev
- A.N. Frumkin Institute of Physical Chemistry and ElectrochemistryRussian Academy of Sciences, Leninskiy prosp., 31 119071 Moscow Russia
| | - Aslan Tsivadze
- A.N. Frumkin Institute of Physical Chemistry and ElectrochemistryRussian Academy of Sciences, Leninskiy prosp., 31 119071 Moscow Russia
| | - Gilles Marcou
- Laboratoire de Chémoinformatique, UMR 7140 CNRSUniversité de Strasbourg 1, rue Blaise Pascal 67000 Strasbourg France
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique, UMR 7140 CNRSUniversité de Strasbourg 1, rue Blaise Pascal 67000 Strasbourg France
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Ahmadi S, Mardinia F, Azimi N, Qomi M, Balali E. Prediction of chalcone derivative cytotoxicity activity against MCF-7 human breast cancer cell by Monte Carlo method. J Mol Struct 2019. [DOI: 10.1016/j.molstruc.2018.12.089] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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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 AND QSAR IN ENVIRONMENTAL RESEARCH 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] [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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The complexation of metal ions with various organic ligands in water: prediction of stability constants by QSPR ensemble modelling. J INCL PHENOM MACRO 2015. [DOI: 10.1007/s10847-015-0543-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Quantitative structure–property relationship study on the intercalation of anticancer drugs with ct-DNA. Med Chem Res 2013. [DOI: 10.1007/s00044-013-0716-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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