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For: Pourbasheer E, Aalizadeh R, Shokouhi Tabar S, Ganjali MR, Norouzi P, Shadmanesh J. 2D and 3D Quantitative Structure–Activity Relationship Study of Hepatitis C Virus NS5B Polymerase Inhibitors by Comparative Molecular Field Analysis and Comparative Molecular Similarity Indices Analysis Methods. J Chem Inf Model 2014;54:2902-14. [DOI: 10.1021/ci500216c] [Cited by in Crossref: 39] [Cited by in F6Publishing: 32] [Article Influence: 4.9] [Reference Citation Analysis]
Number Citing Articles
1 Qasim M, Algamal Z, Ali HM. A binary QSAR model for classifying neuraminidase inhibitors of influenza A viruses (H1N1) using the combined minimum redundancy maximum relevancy criterion with the sparse support vector machine. SAR and QSAR in Environmental Research 2018;29:517-27. [DOI: 10.1080/1062936x.2018.1491414] [Cited by in Crossref: 13] [Article Influence: 3.3] [Reference Citation Analysis]
2 Adhikari N, Amin SA, Saha A, Jha T. Understanding Chemico-Biological Interactions of Glutamate MMP-2 Inhibitors through Rigorous Alignment-Dependent 3D-QSAR Analyses. ChemistrySelect 2017;2:7888-98. [DOI: 10.1002/slct.201701330] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 0.8] [Reference Citation Analysis]
3 Sanyal S, Amin SA, Adhikari N, Jha T. Ligand-based design of anticancer MMP2 inhibitors: a review. Future Med Chem 2021;13:1987-2013. [PMID: 34634916 DOI: 10.4155/fmc-2021-0262] [Reference Citation Analysis]
4 Halder AK, Amin SA, Jha T, Gayen S. Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches. SAR and QSAR in Environmental Research 2017;28:253-73. [DOI: 10.1080/1062936x.2017.1302991] [Cited by in Crossref: 20] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
5 Pourbasheer E, Shokouhi Tabar S, Masand V, Aalizadeh R, Ganjali M. 3D-QSAR and docking studies on adenosine A 2A receptor antagonists by the CoMFA method. SAR and QSAR in Environmental Research 2015;26:461-77. [DOI: 10.1080/1062936x.2015.1049666] [Cited by in Crossref: 12] [Cited by in F6Publishing: 2] [Article Influence: 1.7] [Reference Citation Analysis]
6 Aalizadeh R, Pourbasheer E, Ganjali MR. Analysis of B-Raf $$^{\mathrm{V600E}}$$ V 600 E inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies. Mol Divers 2015;19:915-30. [DOI: 10.1007/s11030-015-9626-y] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
7 Dong MH, Chen HF, Ren YJ, Shao FM. Molecular modeling studies, synthesis and biological evaluation of dabigatran analogues as thrombin inhibitors. Bioorg Med Chem 2016;24:73-84. [PMID: 26690913 DOI: 10.1016/j.bmc.2015.11.025] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 1.4] [Reference Citation Analysis]
8 Li J, Bai F, Liu H, Gramatica P. Ligand Efficiency Outperforms pIC 50 on Both 2D MLR and 3D CoMFA Models: A Case Study on AR Antagonists. Chem Biol Drug Des 2015;86:1501-17. [DOI: 10.1111/cbdd.12619] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 0.4] [Reference Citation Analysis]
9 Algamal ZY, Lee MH, Al-fakih AM, Aziz M. High-dimensional QSAR modelling using penalized linear regression model with L1/2 -norm. SAR and QSAR in Environmental Research 2016;27:703-19. [DOI: 10.1080/1062936x.2016.1228696] [Cited by in Crossref: 10] [Cited by in F6Publishing: 1] [Article Influence: 1.7] [Reference Citation Analysis]
10 Algamal ZY, Lee MH, Al-fakih AM, Aziz M. High-dimensional QSAR prediction of anticancer potency of imidazo[4,5-b]pyridine derivatives using adjusted adaptive LASSO: High-dimensional QSAR prediction. J Chemometrics 2015;29:547-56. [DOI: 10.1002/cem.2741] [Cited by in Crossref: 34] [Cited by in F6Publishing: 9] [Article Influence: 4.9] [Reference Citation Analysis]
11 Algamal ZY, Qasim MK, Ali HTM. A QSAR classification model for neuraminidase inhibitors of influenza A viruses (H1N1) based on weighted penalized support vector machine. SAR and QSAR in Environmental Research 2017;28:415-26. [DOI: 10.1080/1062936x.2017.1326402] [Cited by in Crossref: 11] [Cited by in F6Publishing: 1] [Article Influence: 2.2] [Reference Citation Analysis]
12 Adhikari N, Amin SA, Saha A, Jha T. Structural exploration for the refinement of anticancer matrix metalloproteinase-2 inhibitor designing approaches through robust validated multi-QSARs. Journal of Molecular Structure 2018;1156:501-15. [DOI: 10.1016/j.molstruc.2017.12.005] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
13 Xu A, Zhang Y, Ran T, Liu H, Lu S, Xu J, Xiong X, Jiang Y, Lu T, Chen Y. Quantitative structure–activity relationship study on BTK inhibitors by modified multivariate adaptive regression spline and CoMSIA methods. SAR and QSAR in Environmental Research 2015;26:279-300. [DOI: 10.1080/1062936x.2015.1032346] [Cited by in Crossref: 5] [Article Influence: 0.7] [Reference Citation Analysis]
14 Algamal ZY, Qasim MK, Lee MH, Mohammad Ali HT. Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression. Chemometrics and Intelligent Laboratory Systems 2021;208:104196. [DOI: 10.1016/j.chemolab.2020.104196] [Cited by in Crossref: 9] [Cited by in F6Publishing: 1] [Article Influence: 9.0] [Reference Citation Analysis]
15 Pourbasheer E, Aalizadeh R. 3D-QSAR and molecular docking study of LRRK2 kinase inhibitors by CoMFA and CoMSIA methods. SAR QSAR Environ Res 2016;27:385-407. [PMID: 27228480 DOI: 10.1080/1062936X.2016.1184713] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 1.6] [Reference Citation Analysis]
16 Muhire J, Li BQ, Zhai HL, Li SS, Mi JY. A Simple Approach to the Toxicity Prediction of Anilines and Phenols Towards Aquatic Organisms. Arch Environ Contam Toxicol 2020;78:545-54. [PMID: 31915850 DOI: 10.1007/s00244-019-00703-z] [Reference Citation Analysis]
17 Algamal ZY, Lee MH. A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives. SAR and QSAR in Environmental Research 2017;28:75-90. [DOI: 10.1080/1062936x.2017.1278618] [Cited by in Crossref: 19] [Cited by in F6Publishing: 3] [Article Influence: 3.8] [Reference Citation Analysis]
18 Muhire J, Zhai HL, Lu SH, Li SS, Yin B, Mi JY. The activity prediction of indole inhibitors against HCV NS5B polymerase. Chem Biol Drug Des 2020;95:240-7. [PMID: 31623027 DOI: 10.1111/cbdd.13637] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 0.7] [Reference Citation Analysis]
19 Stitou M, Toufik H, Akabli T, Lamchouri F. Virtual screening of PEBP1 inhibitors by combining 2D/3D-QSAR analysis, hologram QSAR, homology modeling, molecular docking analysis, and molecular dynamic simulations. J Mol Model 2022;28:145. [PMID: 35545728 DOI: 10.1007/s00894-022-05143-6] [Reference Citation Analysis]
20 Rafiei H, Khanzadeh M, Mozaffari S, Bostanifar MH, Avval ZM, Aalizadeh R, Pourbasheer E. QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR). EXCLI J 2016;15:38-53. [PMID: 27065774 DOI: 10.17179/excli2015-731] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
21 Jha T, Adhikari N, Saha A, Amin SA. Multiple molecular modelling studies on some derivatives and analogues of glutamic acid as matrix metalloproteinase-2 inhibitors. SAR and QSAR in Environmental Research 2018;29:43-68. [DOI: 10.1080/1062936x.2017.1406986] [Cited by in Crossref: 9] [Cited by in F6Publishing: 1] [Article Influence: 1.8] [Reference Citation Analysis]
22 Amin SA, Adhikari N, Gayen S, Jha T. Reliable structural information for rational design of benzoxazole type potential cholesteryl ester transfer protein (CETP) inhibitors through multiple validated modeling techniques. Journal of Biomolecular Structure and Dynamics 2019;37:4528-41. [DOI: 10.1080/07391102.2018.1552895] [Cited by in Crossref: 7] [Cited by in F6Publishing: 3] [Article Influence: 2.3] [Reference Citation Analysis]
23 Algamal ZY, Qasim MK, Lee MH, Mohammad Ali HT. High-dimensional QSAR/QSPR classification modeling based on improving pigeon optimization algorithm. Chemometrics and Intelligent Laboratory Systems 2020;206:104170. [DOI: 10.1016/j.chemolab.2020.104170] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 3.5] [Reference Citation Analysis]
24 Algamal ZY, Lee MH. A novel molecular descriptor selection method in QSAR classification model based on weighted penalized logistic regression. Journal of Chemometrics 2017;31:e2915. [DOI: 10.1002/cem.2915] [Cited by in Crossref: 12] [Cited by in F6Publishing: 4] [Article Influence: 2.4] [Reference Citation Analysis]
25 Algamal ZY, Lee MH, Al-fakih AM, Aziz M. High-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty. Journal of Chemometrics 2017;31:e2889. [DOI: 10.1002/cem.2889] [Cited by in Crossref: 14] [Cited by in F6Publishing: 5] [Article Influence: 2.8] [Reference Citation Analysis]
26 Al-fakih AM, Algamal ZY, Lee MH, Abdallah HH, Maarof H, Aziz M. Quantitative structure-activity relationship model for prediction study of corrosion inhibition efficiency using two-stage sparse multiple linear regression: QSAR study using two-stage SMLR. J Chemometrics 2016;30:361-8. [DOI: 10.1002/cem.2800] [Cited by in Crossref: 22] [Cited by in F6Publishing: 8] [Article Influence: 3.7] [Reference Citation Analysis]
27 Algamal Z, Qasim M, Lee M, Ali H. QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm. SAR and QSAR in Environmental Research 2020;31:803-14. [DOI: 10.1080/1062936x.2020.1818616] [Cited by in Crossref: 5] [Article Influence: 2.5] [Reference Citation Analysis]
28 Balasubramanian K, Patil VM. Quantum molecular modeling of hepatitis C virus inhibition through non-structural protein 5B polymerase receptor binding of C5-arylidene rhodanines. Comput Biol Chem 2018;73:147-58. [PMID: 29486389 DOI: 10.1016/j.compbiolchem.2018.01.008] [Reference Citation Analysis]
29 Al-Fakih AM, Algamal ZY, Qasim MK. An improved opposition-based crow search algorithm for biodegradable material classification. SAR QSAR Environ Res 2022;:1-13. [PMID: 35469528 DOI: 10.1080/1062936X.2022.2064546] [Reference Citation Analysis]
30 Amin SA, Adhikari N, Bhargava S, Jha T, Gayen S. Structural exploration of hydroxyethylamines as HIV-1 protease inhibitors: new features identified. SAR QSAR Environ Res 2018;29:385-408. [PMID: 29566580 DOI: 10.1080/1062936X.2018.1447511] [Cited by in Crossref: 14] [Cited by in F6Publishing: 3] [Article Influence: 3.5] [Reference Citation Analysis]
31 Pourbasheer E, Aalizadeh R, Ardabili JS, Ganjali MR. QSPR study on solubility of some fullerenes derivatives using the genetic algorithms — Multiple linear regression. Journal of Molecular Liquids 2015;204:162-9. [DOI: 10.1016/j.molliq.2015.01.028] [Cited by in Crossref: 21] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
32 Amin SA, Adhikari N, Baidya SK, Gayen S, Jha T. Structural refinement and prediction of potential CCR2 antagonists through validated multi-QSAR modeling studies. J Biomol Struct Dyn 2019;37:75-94. [PMID: 29251559 DOI: 10.1080/07391102.2017.1418679] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
33 Adhikari N, Amin SA, Ghosh B, Jha T. Shedding light on designing potential meprin β inhibitors through ligand-based robust validated computational approaches: A proposal to chemists! Journal of Biomolecular Structure and Dynamics 2018;36:3003-22. [DOI: 10.1080/07391102.2017.1374210] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 0.6] [Reference Citation Analysis]
34 Algamal ZY, Lee MH, Al-fakih AM. High-dimensional quantitative structure-activity relationship modeling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on a two-stage adaptive penalized rank regression: QSAR modeling by two-stage adaptive penalized rank regression. J Chemometrics 2016;30:50-7. [DOI: 10.1002/cem.2766] [Cited by in Crossref: 16] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
35 Khamouli S, Belaidi S, Ouassaf M, Lanez T, Belaaouad S, Chtita S. Multi-combined 3D-QSAR, docking molecular and ADMET prediction of 5-azaindazole derivatives as LRRK2 tyrosine kinase inhibitors. Journal of Biomolecular Structure and Dynamics. [DOI: 10.1080/07391102.2020.1824815] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
36 Liu J, Zhu Y, He Y, Zhu H, Gao Y, Li Z, Zhu J, Sun X, Fang F, Wen H, Li W. Combined pharmacophore modeling, 3D-QSAR and docking studies to identify novel HDAC inhibitors using drug repurposing. J Biomol Struct Dyn 2020;38:533-47. [PMID: 30938574 DOI: 10.1080/07391102.2019.1590241] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.7] [Reference Citation Analysis]
37 Adhikari N, Amin SA, Saha A, Jha T. Exploring in house glutamate inhibitors of matrix metalloproteinase-2 through validated robust chemico-biological quantitative approaches. Struct Chem 2018;29:285-97. [DOI: 10.1007/s11224-017-1028-6] [Cited by in Crossref: 12] [Cited by in F6Publishing: 10] [Article Influence: 2.4] [Reference Citation Analysis]
38 Amin SA, Adhikari N, Jha T, Ghosh B. Designing potential HDAC3 inhibitors to improve memory and learning. Journal of Biomolecular Structure and Dynamics 2019;37:2133-42. [DOI: 10.1080/07391102.2018.1477625] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 4.0] [Reference Citation Analysis]
39 Adhikari N, Amin SA, Jha T, Gayen S. Integrating regression and classification-based QSARs with molecular docking analyses to explore the structure-antiaromatase activity relationships of letrozole-based analogs. Can J Chem 2017;95:1285-95. [DOI: 10.1139/cjc-2017-0419] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]