1
|
Tajiani F, Ahmadi S, Lotfi S, Kumar P, Almasirad A. In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization. BMC Chem 2023; 17:87. [PMID: 37496005 PMCID: PMC10373329 DOI: 10.1186/s13065-023-00999-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/30/2023] [Indexed: 07/28/2023] Open
Abstract
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).
Collapse
Affiliation(s)
- Faezeh Tajiani
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
| | - Shahram Lotfi
- Department of Chemistry, Payame Noor University (PNU), Tehran, 19395-4697, Iran
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
| | - Ali Almasirad
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| |
Collapse
|
2
|
Ahmadi S, Lotfi S, Afshari S, Kumar P, Ghasemi E. CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:1013-1031. [PMID: 34875951 DOI: 10.1080/1062936x.2021.2003429] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 11/03/2021] [Indexed: 06/13/2023]
Abstract
Global QSAR modelling was performed to predict the pIC50 values of 233 diverse heterocyclic compounds as BTK inhibitors with the Monte Carlo algorithm of CORAL software using the DCW hybrid descriptors extracted from SMILES notations of molecules. The dataset of 233 BTK inhibitors was randomly split into training, invisible training, calibration and validation sets. The index of ideality of correlation was also applied to build and judge the predictability of the QSAR models. Eight global QSAR models based on the hybrid optimal descriptor using two target functions, i.e. TF1 (WIIC = 0) and TF2 (WIIC = 0.2) have been constructed. The statistical parameters of QSAR models computed by TF2 are more reliable and robust and were used to predict the pIC50 values. The model constructed for split 4 via TF2 is regarded as the best model and the numerical values of r2Train, r2Valid, Q2Train and Q2Valid are equal to 0.7981, 0.7429, 0.7898 and 0.6784, respectively. By internal and external validation techniques, the predictability and reliability of the designed models have been assessed. The structural attributes responsible for the increase and decrease of pIC50 of BTK inhibitors were also identified.
Collapse
Affiliation(s)
- S Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - S Lotfi
- Department of Chemistry, Payame Noor University (PNU), Tehran, Iran
| | - S Afshari
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - P Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, India
| | - E Ghasemi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| |
Collapse
|
3
|
Ahmadi S, Lotfi S, Kumar P. Quantitative structure-toxicity relationship models for predication of toxicity of ionic liquids towards Leukemia rat cell line IPC-81 based on index of ideality of correlation. Toxicol Mech Methods 2021; 32:302-312. [PMID: 34724871 DOI: 10.1080/15376516.2021.2000686] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
The application of ion 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 minimise 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 lineIIC is tested as a criterion of predictive potential.The toxicological effects of ILs are discussed based on the proposed model.
Collapse
Affiliation(s)
- Shahin Ahmadi
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Chemistry, Tehran medical sciences, Islamic Azad University, Tehran, Iran
| | - Shahram Lotfi
- Department of Chemistry, Payame Noor University (PNU), 19395-4697 Tehran, Iran
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, Haryana, 136119, India
| |
Collapse
|
4
|
Lotfi S, Ahmadi S, Kumar P. A hybrid descriptor based QSPR model to predict the thermal decomposition temperature of imidazolium ionic liquids using Monte Carlo approach. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116465] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
5
|
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]
|
6
|
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 AND QSAR IN ENVIRONMENTAL RESEARCH 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] [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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
|
8
|
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]
|
9
|
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]
|
10
|
Ahmadi S, Babaee E, khazaei MR. Application of self organizing maps and GA-MLR for the estimation of stability constant of 18-crown-6 ether derivatives with sodium cation. J INCL PHENOM MACRO 2013. [DOI: 10.1007/s10847-013-0337-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|