QSAR study of tetrahydropteridin derivatives as polo-like kinase 1(PLK1) Inhibitors with molecular docking and dynamics study

References

• G. de Cárcer, G. Manning, and M. Malumbres, From Plk1 to Plk5, Cell Cycle 10 (2011), pp. 2255–2262. doi:10.4161/cc.10.14.16494.
   PubMed Web of Science ®Google Scholar
• P.R. Duchowicz, Linear regression QSAR models for polo-like kinase-1 inhibitors, Cells 7 (2018), pp. 13. doi:10.3390/cells7020013.
   PubMed Web of Science ®Google Scholar
• S. Bhujbal, S. Keretsu, and S. Cho, A Combined molecular docking and 3D‐QSAR studies on tetrahydropteridin derivatives as PLK2 antagonists, Bull. Korean Chem. Soc. 40 (2019), pp. 796–802. doi:10.1002/bkcs.11824.
   Web of Science ®Google Scholar
• R. Shahin, N.N. Al-Hashimi, N.E.-H. Daoud, S. Aljamal, and O. Shaheen, QSAR-guided pharmacophoric modeling reveals important structural requirements for Polo kinase 1 (Plk1) inhibitors, J. Mol. Graph. Model. 109 (2021), pp. 108022. doi:10.1016/j.jmgm.2021.108022.
   PubMed Web of Science ®Google Scholar
• R. Chekkara, N. Kandakatla, V.R. Gorla, S.R. Tenkayala, and E. Susithra, Theoretical studies on benzimidazole and imidazo[1,2-a]pyridine derivatives as Polo-like kinase 1 (Plk1) inhibitors: Pharmacophore modeling, atom-based 3D-QSAR and molecular docking approach, J. Saudi Chem. Soc. 21 (2017), pp. S311–S321. doi:10.1016/j.jscs.2014.03.007.
   Web of Science ®Google Scholar
• Y. Kong and A. Yan, QSAR models for predicting the bioactivity of Polo-like Kinase 1 inhibitors, Chemom. Intell. Lab. Syst. 167 (2017), pp. 214–225. doi:10.1016/j.chemolab.2017.06.011.
   Web of Science ®Google Scholar
• Z. Li, L. Xu, L. Zhu, Y. Zhao, T. Hu, B. Yin, Y. Liu, and Y. Hou, Design, synthesis and biological evaluation of novel pteridinone derivatives possessing a hydrazone moiety as potent PLK1 inhibitors, Bioorg. Med. Chem. Lett. 30 (2020), pp. 127329. doi:10.1016/j.bmcl.2020.127329.
   PubMedGoogle Scholar
• L. Li, W. Xue, Z. Shen, J. Liu, M. Hu, Z. Cheng, Y. Wang, Y. Chen, H. Chang, Y. Liu, B. Liu, and J. Zhao, A cereblon modulator CC-885 induces CRBN- and p97-dependent PLK1 degradation and synergizes with volasertib to suppress lung cancer, Mol. Ther. Oncol. 18 (2020), pp. 215–225. doi:10.1016/j.omto.2020.06.013.
   PubMed Web of Science ®Google Scholar
• S. Al-Assaf, A. Abuhammad, M. Hijjawi, and M. Taha, Structure-based discovery of new polo-like kinase 1 (PLK1) inhibitors as potential anticancer agents via docking-based comparative intermolecular contacts analysis (dbCICA), Med. Chem. Res. 30 (2021), pp. 1747–1766. doi:10.1007/s00044-021-02774-x.
   Web of Science ®Google Scholar
• J. Sun, P.-C. Lv, F.-J. Guo, X.-Y. Wang, X. Han, Y. Zhang, G.-H. Sheng, -S.-S. Qian, and H.-L. Zhu, Aromatic diacylhydrazine derivatives as a new class of polo-like kinase 1 (PLK1) inhibitors, Eur. J. Med. Chem. 81 (2014), pp. 420–426. doi:10.1016/j.ejmech.2014.05.026.
   PubMed Web of Science ®Google Scholar
• A. Joshi, H. Bhojwani, and U. Joshi, Strategies to select the best pharmacophore model: A case study in pyrazoloquinazoline class of PLK-1 inhibitors, Med. Chem. Res. 27 (2018), pp. 234–260. doi:10.1007/s00044-017-2057-9.
   Web of Science ®Google Scholar
• S. Lu, H.-C. Liu, Y.-D. Chen, H.-L. Yuan, S.-L. Sun, Y.-P. Gao, P. Yang, L. Zhang, and T. Lu, Combined pharmacophore modeling, docking, and 3D-QSAR studies of PLK1 inhibitors, Int. J. Mol. Sci. 12 (2011), pp. 8713–8739. doi:10.3390/ijms12128713.
   PubMed Web of Science ®Google Scholar
• Z. Pei, J. Ning, N. Zhang, X. Zhang, H. Zhang, and R. Zhang, Genetic instability of lung induced by carbon black nanoparticles is related with Plk1 signals changes, NanoImpact 26 (2022), pp. 100400. doi:10.1016/j.impact.2022.100400.
   PubMed Web of Science ®Google Scholar
• B. Cholewa, M. Ndiaye, W. Huang, X. Liu, and N. Ahmad, Small molecule inhibition of polo-like kinase 1 by volasertib (BI 6727) causes significant melanoma growth delay and regression in vivo, Cancer Lett. 385 (2016), pp. 179–187. doi:10.1016/j.canlet.2016.10.025.
   PubMed Web of Science ®Google Scholar
• R. Affatato, L. Carrassa, R. Chilà, M. Lupi, V. Restelli, and G. Damia, Identification of PLK1 as a new therapeutic target in mucinous ovarian carcinoma, Cancers 12 (2020), pp. 672. doi:10.3390/cancers12030672.
   PubMed Web of Science ®Google Scholar
• S. Liu, H. Yosief, L. Dai, H. Huang, G. Dhawan, X. Zhang, A. Muthengi, J. Roberts, D. Buckley, J. Perry, L. Wu, J. Bradner, J. Qi, and W. Zhang, Structure-guided design and development of potent and selective dual bromodomain 4 (BRD4)/polo-like kinase 1 (PLK1) Inhibitors, J. Med. Chem. 61 (2018), pp. 7785–7795. doi:10.1021/acs.jmedchem.8b00765.
   PubMed Web of Science ®Google Scholar
• J. Fernández-Sainz, P.J. Pacheco-Liñán, J.M. Granadino-Roldán, I. Bravo, J. Rubio-Martínez, J. Albaladejo, and A. Garzón-Ruiz, Shedding light on the binding mechanism of kinase inhibitors BI-2536, Volasetib and Ro-3280 with their pharmacological target PLK1, J. Photochem. Photobiol. B: Biol. 232 (2022), pp. 112477. doi:10.1016/j.jphotobiol.2022.112477.
   PubMed Web of Science ®Google Scholar
• J. Tong, T. Wang, and Y. Feng, Drug design and molecular docking simulations of Polo-like kinase 1 inhibitors based on QSAR study, New J. Chem. 44 (2020), pp. 21134–21145. doi:10.1039/D0NJ04367B.
   Web of Science ®Google Scholar
• Z. Deng, G. Chen, S. Liu, Y. Li, J. Zhong, B. Zhang, H. Huang, Z. Wang, Q. Xu, and X. Deng, Discovery of Methyl 3-((2-((1-(dimethylglycyl)-5-methoxyindolin-6-yl)amino)-5-(trifluoro-methyl)pyrimidin-4-yl)amino)thiophene-2-carboxylate as a potent and selective polo-like Kinase 1 (PLK1) inhibitor for combating hepatocellular carcinoma, Eur. J. Med. Chem. 206 (2020), pp. 112697. doi:10.1016/j.ejmech.2020.112697.
   PubMed Web of Science ®Google Scholar
• S. Su, G. Chhabra, C.K. Singh, M.A. Ndiaye, and N. Ahmad, PLK1 inhibition-based combination therapies for cancer management, Transl. Oncol. 16 (2022), pp. 101332. doi:10.1016/j.tranon.2021.101332.
   PubMed Web of Science ®Google Scholar
• Y. Oh, H. Jung, H. Kim, J. Baek, J. Jun, H. Cho, D. Im, and J.-M. Hah, Design and synthesis of a novel PLK1 inhibitor scaffold using a hybridized 3D-QSAR model, Int. J. Mol. Sci. 22 (2021), pp. 3865. doi:10.3390/ijms22083865.
   PubMed Web of Science ®Google Scholar
• J. Tong, D. Luo, S. Bian, and X. Zhang, Structural investigation of tetrahydropteridin analogues as selective PLK1 inhibitors for treating cancer through combined QSAR techniques, molecular docking, and molecular dynamics simulations, J. Mol. Liq. 335 (2021), pp. 116235. doi:10.1016/j.molliq.2021.116235.
   Web of Science ®Google Scholar
• S. Ahmadi, S. Lotfi, S. Afshari, P. Kumar, and E. Ghasemi, CORAL: Monte Carlo based global QSAR modelling of Bruton tyrosine kinase inhibitors using hybrid descriptors, SAR QSAR Environ. Res. 32 (2021), pp. 1013–1031. doi:10.1080/1062936X.2021.2003429.
   PubMed Web of Science ®Google Scholar
• S. Ahmadi, S. Lotfi, and P. Kumar, A Monte Carlo method based QSPR model for prediction of reaction rate constants of hydrated electrons with organic contaminants, SAR QSAR Environ. Res. 31 (2020), pp. 935–950. doi:10.1080/1062936X.2020.1842495.
   PubMed Web of Science ®Google Scholar
• A. Kumar and P. Kumar, 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. 32 (2021), pp. 817–834. doi:10.1080/1062936X.2021.1973095.
   PubMed Web of Science ®Google Scholar
• P. Kumar and A. Kumar, In silico enhancement of azo dye adsorption affinity for cellulose fibre through mechanistic interpretation under guidance of QSPR models using Monte Carlo method with index of ideality correlation, SAR QSAR Environ. Res. 31 (2020), pp. 697–715. doi:10.1080/1062936X.2020.1806105.
   PubMed Web of Science ®Google Scholar
• P. Kumar, A. Kumar, and J. Sindhu, In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method, SAR QSAR Environ. Res. 30 (2019), pp. 525–541. doi:10.1080/1062936X.2019.1629998.
   PubMed Web of Science ®Google Scholar
• P. Kumar, A. Kumar, and J. Sindhu, 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. 30 (2019), pp. 63–80. doi:10.1080/1062936X.2018.1564067.
   PubMed Web of Science ®Google Scholar
• P. Kumar, R. Singh, A. Kumar, A.P. Toropova, A.A. Toropov, M. Devi, S. Lal, J. Sindhu, and D. Singh, 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. 33 (2022), pp. 677–700. doi:10.1080/1062936X.2022.2120068.
   PubMed Web of Science ®Google Scholar
• M.S. Chauhan, P. Kumar, and A. Kumar, Development of prediction model for fructose-1,6- bisphosphatase inhibitors using the Monte Carlo method, SAR QSAR Environ. Res. 30 (2019), pp. 145–159. doi:10.1080/1062936X.2019.1568299.
   PubMed Web of Science ®Google Scholar
• X.-Y. Wang, Y.-H. Wang, Z. Song, X.-Y. Hu, J.-P. Wei, J. Zhang, and H.-S. Wang, Recent progress in functional peptides designed for tumor-targeted imaging and therapy, J. Mater. Chem. C 9 (2021), pp. 3749–3772. doi:10.1039/D0TC05405D.
   Web of Science ®Google Scholar
• A. Nath, P. De, and K. Roy, In silico modelling of acute toxicity of 1, 2, 4-triazole antifungal agents towards zebrafish (Danio rerio) embryos: Application of the small dataset modeller tool, Toxicol. In Vitro 75 (2021), pp. 105205. doi:10.1016/j.tiv.2021.105205.
   PubMed Web of Science ®Google Scholar
• M. Duhan, J. Sindhu, P. Kumar, M. Devi, R. Singh, R. Kumar, S. Lal, A. Kumar, S. Kumar, and K. Hussain, Quantitative structure activity relationship studies of novel hydrazone derivatives as α-amylase inhibitors with index of ideality of correlation, J. Biomol. Struct. Dyn. 40 (2022), pp. 4933–4953. doi:10.1080/07391102.2020.1863861.
   PubMed Web of Science ®Google Scholar
• S.C. Peter, J.K. Dhanjal, V. Malik, N. Radhakrishnan, M. Jayakanthan, and D. Sundar, Quantitative structure-activity relationship (QSAR): Modeling approaches to biological applications, in Encyclopedia of Bioinformatics and Computational Biology, S. Ranganathan, M. Gribskov, K. Nakai, and C. Schonbach, eds., Academic Press, Oxford, 2019, pp. 661–676. doi:10.1016/B978-0-12-809633-8.20197-0.
   Google Scholar
• M. Babu Singh, P. Jain, J. Tomar, V. Kumar, I. Bahadur, D.K. Arya, and P. Singh, An In Silico investigation for Acyclovir and its derivatives to fight the COVID-19: Molecular docking, DFT calculations, ADME and td-molecular dynamics simulations, J. Indian Chem. Soc. 99 (2022), pp. 100433. doi:10.1016/j.jics.2022.100433.
   Web of Science ®Google Scholar
• G. Zhang, N. Li, Y. Zhang, J. Pan, and D. Gong, Binding mechanism of 4−octylphenol with human serum albumin: Spectroscopic investigations, molecular docking and dynamics simulation, Spectrochim. Acta A: Mol. Biomol. Spectrosc. 255 (2021), pp. 119662. doi:10.1016/j.saa.2021.119662.
   PubMed Web of Science ®Google Scholar
• -M.-M. Zhan, Y. Yang, J. Luo, -X.-X. Zhang, X. Xiao, S. Li, K. Cheng, Z. Xie, Z. Tu, and C. Liao, Design, synthesis, and biological evaluation of novel highly selective polo-like kinase 2 inhibitors based on the tetrahydropteridin chemical scaffold, Eur. J. Med. Chem. 143 (2018), pp. 724–731. doi:10.1016/j.ejmech.2017.11.058.
   PubMed Web of Science ®Google Scholar
• X. Lv, X. Yang, -M.-M. Zhan, P. Cao, S. Zheng, R. Peng, J. Han, Z. Xie, Z. Tu, and C. Liao, Structure-based design and SAR development of novel selective polo-like kinase 1 inhibitors having the tetrahydropteridin scaffold, Eur. J. Med. Chem. 184 (2019), pp. 111769. doi:10.1016/j.ejmech.2019.111769.
   PubMed Web of Science ®Google Scholar
• R.L. Bakal, R.D. Jawarkar, J.V. Manwar, M.S. Jaiswal, A. Ghosh, A. Gandhi, M.E.A. Zaki, S. Al-Hussain, A. Samad, V.H. Masand, N. Mukerjee, S. Nasir Abbas Bukhari, P. Sharma, and I. Lewaa, Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual screening, molecular docking, MD simulation and MMGBSA approaches, Saudi Pharm. 30 (2022), pp. 693–710. doi:10.1016/j.jsps.2022.04.003.
   PubMed Web of Science ®Google Scholar
• S. Pramanik and K. Roy, Modeling bioconcentration factor (BCF) using mechanistically interpretable descriptors computed from open source tool “PaDEL-Descriptor”, Environ. Sci. Pollut. Res. 21 (2013), pp. 2955–2965. doi:10.1007/s11356-013-2247-z.
   PubMed Web of Science ®Google Scholar
• C.W. Yap, PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints, J. Comput. Chem. 32 (2011), pp. 1466–1474. doi:10.1002/jcc.21707.
   PubMed Web of Science ®Google Scholar
• V.H. Masand, N.N.E. El-Sayed, M.U. Bambole, V.R. Patil, and S.D. Thakur, Multiple quantitative structure-activity relationships (QSARs) analysis for orally active trypanocidal N-myristoyltransferase inhibitors, J. Mol. Struct. 1175 (2019), pp. 481–487. doi:10.1016/j.molstruc.2018.07.080.
   Web of Science ®Google Scholar
• V.H. Masand, N.N.E. El-Sayed, D.T. Mahajan, A.G. Mercader, A.M. Alafeefy, and I.G. Shibi, QSAR modeling for anti-human African trypanosomiasis activity of substituted 2-Phenylimidazopyridines, J. Mol. Struct. 1130 (2017), pp. 711–718. doi:10.1016/j.molstruc.2016.11.012.
   Web of Science ®Google Scholar
• A. Seth and K. Roy, QSAR modeling of algal low level toxicity values of different phenol and aniline derivatives using 2D descriptors, Aquat. Toxicol. 228 (2020), pp. 105627. doi:10.1016/j.aquatox.2020.105627.
   PubMed Web of Science ®Google Scholar
• D.E. Arthur, M.E.S. Soliman, S.E. Adeniji, O. Adedirin, and F. Peter, QSAR and molecular docking study of gonadotropin-releasing hormone receptor inhibitors, Sci. Afr. 17 (2022), pp. e01291.
   Google Scholar
• P. Gopinath and M.K. Kathiravan, Molecular insights of oxadiazole benzene sulfonamides as human carbonic anhydrase IX inhibitors: Combined molecular docking, molecular dynamics, and 3D QSAR studies, J. Indian Chem. Soc. 99 (2022), pp. 100339. doi:10.1016/j.jics.2022.100339
   Web of Science ®Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276. doi:10.1016/S1093-3263(01)00123-1.
   PubMed Web of Science ®Google Scholar
• G.M. Morris, R. Huey, W. Lindstrom, M.F. Sanner, R.K. Belew, D.S. Goodsell, and A.J. Olson, AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility, J. Comput. Chem. 30 (2009), pp. 2785–2791. doi:10.1002/jcc.21256.
   PubMed Web of Science ®Google Scholar
• S. Yuan, H.C.S. Chan, and Z. Hu, Using PyMOL as a platform for computational drug design, WIREs Comput. Mol. Sci. 7 (2017), pp. e1298. doi:10.1002/wcms.1298.
   Google Scholar
• E. Pettersen, T. Goddard, C. Huang, G. Couch, D. Greenblatt, E. Meng, and T. Ferrin, UCSF chimera – A visualization system for exploratory research and analysis, J. Comput. Chem. 25 (2004), pp. 1605–1612. doi:10.1002/jcc.20084.
   PubMed Web of Science ®Google Scholar
• P. Rani, K.S. Chahal, P.R. Kataria, P. Kumar, S. Kumar, and J. Sindhu, Unravelling the thermodynamics and binding interactions of bovine serum albumin (BSA) with thiazole based carbohydrazide: Multi-spectroscopic, DFT and molecular dynamics approach, J. Mol. Struct. 1270 (2022), pp. 133939. doi:10.1016/j.molstruc.2022.133939.
   Web of Science ®Google Scholar
• S. Genheden and U. Ryde, The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities, Expert Opin. Drug Discov. 10 (2015), pp. 449–461. doi:10.1517/17460441.2015.1032936.
   PubMed Web of Science ®Google Scholar
• D.L. Beveridge and F.M. DiCapua, Free energy via molecular simulation: Applications to chemical and biomolecular systems, Annu. Rev. Biophys. Biophys. Chem. 18 (1989), pp. 431–492. doi:10.1146/annurev.bb.18.060189.002243.
   PubMedGoogle Scholar
• C. Jarzynski, Nonequilibrium equality for free energy differences, Phys. Rev. Lett. 78 (1997), pp. 2690–2693. doi:10.1103/PhysRevLett.78.2690.
   Web of Science ®Google Scholar
• R.A. Friesner, R.B. Murphy, M.P. Repasky, L.L. Frye, J.R. Greenwood, T.A. Halgren, P.C. Sanschagrin, and D.T. Mainz, Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein−ligand complexes, J. Med. Chem. 49 (2006), pp. 6177–6196. doi:10.1021/jm051256o.
   PubMed Web of Science ®Google Scholar
• S. Ahmed, M.C. Ali, R.A. Ruma, S. Mahmud, G.K. Paul, M.A. Saleh, M.M. Alshahrani, A.J. Obaidullah, S.K. Biswas, M.M. Rahman, M.M. Rahman, and M.R. Islam, Molecular docking and dynamics simulation of natural compounds from betel leaves (Piper betle L.) for investigating the potential inhibition of alpha-amylase and alpha-glucosidase of type 2 diabetes, Molecules 27 (2022), pp. 4526. doi:10.3390/molecules27144526.
   PubMed Web of Science ®Google Scholar
• S. Sirin, D.A. Pearlman, and W. Sherman, Physics-based enzyme design: Predicting binding affinity and catalytic activity, Proteins: Struct. Funct. Bioinform. 82 (2014), pp. 3397–3409. doi:10.1002/prot.24694.
   PubMed Web of Science ®Google Scholar
• R. Pingaew, V. Prachayasittikul, A. Worachartcheewan, A. Thongnum, S. Prachayasittikul, S. Ruchirawat, and V. Prachayasittikul, Anticancer activity and QSAR study of sulfur-containing thiourea and sulfonamide derivatives, Heliyon 8 (2022), pp. e10067. doi:10.1016/j.heliyon.2022.e10067.
   PubMed Web of Science ®Google Scholar
• T. Puzyn, J. Leszczynski, and M.T. Cronin, Recent Advances in QSAR Studies: Methods and Applications, Vol. 8, Springer Science & Business Media, London, 2010.
   Google Scholar
• B. Hollas, An analysis of the autocorrelation descriptor for molecules, J. Math. Chem. 33 (2003), pp. 91–101. doi:10.1023/A:1023247831238.
   Web of Science ®Google Scholar
• Danishuddin and A.U. Khan, Descriptors and their selection methods in QSAR analysis: Paradigm for drug design, Drug Discov. Today 21 (2016), pp. 1291–1302. doi:10.1016/j.drudis.2016.06.013.
   PubMed Web of Science ®Google Scholar
• L.H. Hall and L.B. Kier, Electrotopological state indices for atom types: A novel combination of electronic, topological, and valence state information, J. Chem. Inf. Comput. Sci. 35 (1995), pp. 1039–1045. doi:10.1021/ci00028a014.
   Google Scholar
• L. Saíz-Urra, M. González, and M. Teijeira, 2D-autocorrelation descriptors for predicting cytotoxicity of naphthoquinone ester derivatives against oral human epidermoid carcinoma, Bioorg. Med. Chem. 15 (2007), pp. 3565–3571. doi:10.1016/j.bmc.2007.02.032.
   PubMed Web of Science ®Google Scholar
• H.M. Kasralikar, S.C. Jadhavar, S.V. Goswami, N.S. Kaminwar, and S.R. Bhusare, Design, synthesis and molecular docking of pyrazolo [3,4d] thiazole hybrids as potential anti-HIV-1 NNRT inhibitors, Bioorg. Chem. 86 (2019), pp. 437–444. doi:10.1016/j.bioorg.2019.02.006.
   PubMed Web of Science ®Google Scholar
• W. Gao, X. Ma, H. Yang, Y. Luan, and H. Ai, Molecular engineering and activity improvement of acetylcholinesterase inhibitors: Insights from 3D-QSAR, docking, and molecular dynamics simulation studies, J. Mol. Graph. Model. 116 (2022), pp. 108239. doi:10.1016/j.jmgm.2022.108239.
   PubMed Web of Science ®Google Scholar
• S. Jana, S. Dalapati, S. Ghosh, and N. Guchhait, Binding interaction between plasma protein bovine serum albumin and flexible charge transfer fluorophore: A spectroscopic study in combination with molecular docking and molecular dynamics simulation, J. Photochem. Photobiol. A: Chem. 231 (2012), pp. 19–27. doi:10.1016/j.jphotochem.2011.12.002.
   Web of Science ®Google Scholar
• L. Li, C.E. Peng, Y. Wang, C. Xiong, Y. Liu, C. Wu, and J. Wang, Identify promising IKK-β inhibitors: A docking-based 3D-QSAR study combining molecular design and molecular dynamics simulation, Arab. J. Chem. 15 (2022), pp. 103786. doi:10.1016/j.arabjc.2022.103786.
   Web of Science ®Google Scholar