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Research Article

Hybrid consensus and k-nearest neighbours (kNN) strategies to classify dual BRD4/PLK1 inhibitors

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Pages 779-792 | Received 12 Sep 2022, Accepted 17 Oct 2022, Published online: 04 Nov 2022

References

  • T. Stankovic, J. Dinić, A. Podolski-Renic, L. Musso, S. Buric, S. Dallavalle, and M. Pesic, Dual inhibitors as a new challenge for cancer multidrug resistance treatment, Curr. Med. Chem. 26 (2019), pp. 6074–6106. doi:10.2174/0929867325666180607094856.
  • J. Liu, S. Teng, L. Fei, W. Zhang, X. Fang, Z. Zhang, and N. Wu, A novel consensus learning approach to incomplete multi-view clustering, Pattern Recogn. 115 (2021), pp. 107890. doi:10.1016/j.patcog.2021.107890.
  • R. Qaddoura, H. Faris, and I. Aljarah, An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio, Int. J. Mach. Learn. 11 (2020), pp. 675–714. doi:10.1007/s13042-019-01027-z.
  • Z. Wu, B. Jin, H. Fujita, and J. Xu, Consensus analysis for AHP multiplicative preference relations based on consistency control: A heuristic approach, Knowl. Based Syst. 191 (2020), pp. 105317. doi:10.1016/j.knosys.2019.105317.
  • L. Aguilera-Mendoza, Y. Marrero-Ponce, C.R. García-Jacas, E. Chavez, J.A. Beltran, H.A. Guillen-Ramirez, and C.A. Brizuela, Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: An unsupervised learning approach, Sci. Rep. 10 (2020), pp. 1–23. doi:10.1038/s41598-020-75029-1.
  • A. Yoshimori, H. Hu, and J. Bajorath, Adapting the DeepSARM approach for dual-target ligand design, J. Comput. Aided Mol. 35 (2021), pp. 587–600. doi:10.1007/s10822-021-00379-5.
  • P. Tang, J. Zhang, J. Liu, C.M. Chiang, and L. Ouyang, Targeting bromodomain and extraterminal proteins for drug discovery: From current progress to technological development, J. Med. Chem. 64 (2021), pp. 2419–2435. doi:10.1021/acs.jmedchem.0c01487.
  • N.Y. Wang, Y. Xu, K.J. Xiao, W.Q. Zuo, Y.X. Zhu, R. Hu, W.L. Wang, Y.J. Shi, L.T. Yu, and Z.H. Liu, Design, synthesis, and biological evaluation of 4, 5-dihydro-[1, 2, 4]triazolo [4, 3-f] pteridine derivatives as novel dual-PLK1/BRD4 inhibitors, Eur. J. Med. Chem. 191 (2020), pp. 112152. doi:10.1016/j.ejmech.2020.112152.
  • S. Liu, H.O. Yosief, L. Dai, H. Huang, G. Dhawan, X. Zhang, A.M. Muthengi, J. Roberts, D.L. Buckley, J.A. Perry, L. Wu, J.E. 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.
  • E. Watts, D. Heidenreich, E. Tucker, M. Raab, K. Strebhardt, L. Chesler, S. Knapp, B. Bellenie, and S. Hoelder, Designing dual inhibitors of anaplastic lymphoma kinase (ALK) and bromodomain-4 (BRD4) by tuning kinase selectivity, J. Med. Chem. 62 (2019), pp. 2618–2637. doi:10.1021/acs.jmedchem.8b01947.
  • J.B. Tong, D. Luo, Y. Feng, S. Bian, X. Zhang, and T.H. Wang, Structural modification of 4, 5-dihydro-[1, 2, 4] triazolo [4, 3-f] pteridine derivatives as BRD4 inhibitors using 2D/3D-QSAR and molecular docking analysis, Mol. Divers. 25 (2021), pp. 1855–1872. doi:10.1007/s11030-020-10172-5.
  • D. Luo, J.B. Tong, X.C. Xiao, S. Bian, X. Zhang, J. Wang, and H.Y. Xu, Theoretically exploring selective-binding mechanisms of BRD4 through integrative computational approaches, SAR. QSAR Environ. Res. 32 (2021), pp. 985–1011. doi:10.1080/1062936X.2021.1999317.
  • J.B. 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.
  • 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.
  • X. Mu, L. Bai, Y. Xu, J. Wang, and H. Lu, Protein targeting chimeric molecules specific for dual bromodomain 4 (BRD4) and polo-like kinase 1 (PLK1) proteins in acute myeloid leukemia cells, Biochem. Biophys. Res. 521 (2020), pp. 833–839. doi:10.1016/j.bbrc.2019.11.007.
  • M. Asadollahi-Baboli and S. Dehnavi, Docking and QSAR analysis of tetracyclic oxindole derivatives as α-glucosidase inhibitors, Comput. Biol. Chem. 76 (2018), pp. 283–292. doi:10.1016/j.compbiolchem.2018.07.019.
  • M. Asadollahi-Baboli, In silico evaluation, molecular docking and QSAR analysis of quinazoline-based EGFR-T790M inhibitors, Mol. Divers. 20 (2016), pp. 729–739. doi:10.1007/s11030-016-9672-0.
  • F. Grisoni, V. Consonni, and R. Todeschini, Impact of molecular descriptors on computational models, in Computational Chemogenomics, J.B. Brown (ed.), Humana Press, New York, NY, pp. 171–209, 2018.
  • I. Triguero, D. García‐Gil, J. Maillo, J. Luengo, S. García, and F. Herrera, Transforming big data into smart data: An insight on the use of the k‐nearest neighbors algorithm to obtain quality data, Data Min. Knowl. Discov. 9 (2019), pp. 1289.
  • D. Ballabio and V. Consonni, Classification tools in chemistry. Part 1: Linear models. PLS-DA, Anal. Meth. 5 (2013), pp. 3790–3798. doi:10.1039/c3ay40582f.
  • T.B. Chandra, K. Verma, B.K. Singh, D. Jain, and S.S. Netam, Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble, Expert Syst. Appl. 165 (2021), pp. 113909. doi:10.1016/j.eswa.2020.113909.
  • F. Grisoni, V. Consonni, and D. Ballabio, Machine learning consensus to predict the binding to the androgen receptor within the CoMPARA project, J. Chem. Inf. Model. 59 (2019), pp. 1839–1848. doi:10.1021/acs.jcim.8b00794.
  • D. Ballabio, F. Grisoni, V. Consonni, and R. Todeschini, Integrated QSAR models to predict acute oral systemic toxicity, Mol. Inform. 38 (2019), pp. 1800124. doi:10.1002/minf.201800124.
  • C. Valsecchi, F. Grisoni, V. Consonni, and D. Ballabio, Consensus versus individual QSARs in classification: Comparison on a large-scale case study, J. Chem. Inf. Model. 60 (2020), pp. 1215–1223. doi:10.1021/acs.jcim.9b01057.
  • M. Gholamhoseinnia and M. Asadollahi-Baboli, Ranked binding energies of residues and data fusion to identify the active and selective pyrimidine-based Janus kinases 3 (JAK3) inhibitors, SAR QSAR Environ. Res. 33 (2022), pp. 23–34. doi:10.1080/1062936X.2021.2013318.
  • M. Asadollahi-Baboli and A. Mani-Varnosfaderani, Therapeutic index modeling and predictive QSAR of novel thiazolidin-4-one analogs against Toxoplasma gondii, Eur. J. Pharm. 70 (2015), pp. 117–124. doi:10.1016/j.ejps.2015.01.014.
  • J. Li, M. Gao, and R. D’Agostino, Evaluating classification accuracy for modern learning approaches, Stat. Med. 38 (2019), pp. 2477–2503. doi:10.1002/sim.8103.
  • K. Seraj and M. Asadollahi-Baboli, In silico evaluation of 5-hydroxypyrazoles as LSD1 inhibitors based on molecular docking derived descriptors, J. Mol. Struct. 1179 (2019), pp. 514–524. doi:10.1016/j.molstruc.2018.11.019.
  • J. Novak, M.A. Grishina, V.A. Potemkin, and J. Gasteiger, Performance of radial distribution function-based descriptors in the chemoinformatic studies of HIV-1 protease, Future Med. Chem. 12 (2020), pp. 299–309. doi:10.4155/fmc-2019-0241.
  • Z. Kalaki and M. Asadollahi-Baboli, Molecular docking-based classification and systematic QSAR analysis of indoles as Pim kinase inhibitors, SAR QSAR Environ. Res. 31 (2020), pp. 399–419. doi:10.1080/1062936X.2020.1751277.

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