Rini BI, Campbell SC, Escudier B. Renal cell carcinoma. The Lancet. 2009;373(9669):1119–32. https://doi.org/10.1016/s0140-6736(09)60229-4.

Article  CAS  Google Scholar 

Motzer RJ, Rini BI, McDermott DF, Redman BG, Kuzel TM, Harrison MR, Vaishampayan UN, Drabkin HA, George S, Logan TF, Margolin KA, Plimack ER, Lambert AM, Waxman IM, Hammers HJ. Nivolumab for metastatic renal cell carcinoma: results of a randomized phase II trial. J Clin Oncol. 2015;33(13):1430–7. https://doi.org/10.1200/jco.2014.59.0703.

Article  CAS  PubMed  Google Scholar 

Hsieh JJ, Purdue MP, Signoretti S, Swanton C, Albiges L, Schmidinger M, Heng DY, Larkin J, Ficarra V. Renal cell carcinoma. Nat Rev Dis Prime. 2017. https://doi.org/10.1038/nrdp.2017.9.

Article  Google Scholar 

Broekman F. Tyrosine kinase inhibitors: multi-targeted or single-targeted? World J Clin Oncol. 2011;2(2):80. https://doi.org/10.5306/wjco.v2.i2.80.

Article  PubMed  PubMed Central  Google Scholar 

Heng DY, Xie W, Regan MM, Warren MA, Golshayan AR, Sahi C, Eigl BJ, Ruether JD, et al. Prognostic factors for overall survival in patients With metastatic renal cell carcinoma treated with vascular endothelial growth factor–targeted agents: results from a large, multicenter study. J Clin Oncol. 2009;27(34):5794–9. https://doi.org/10.1200/jco.2008.21.4809.

Hutson TE, Davis ID, Machiels JH, de Souza PL, Baker K, Bordogna W, Westlund R, Crofts T, Pandite L, Figlin RA. Biomarker analysis and final efficacy and safety results of a phase II renal cell carcinoma trial with pazopanib (GW786034), a multi-kinase angiogenesis inhibitor. J Clin Oncol. 2008;26:5046–504. https://doi.org/10.1200/jco.2008.26.15_suppl.5046.

Article  Google Scholar 

Hasinoff BB, Patel D. The lack of target specificity of small molecule anticancer kinase inhibitors is correlated with their ability to damage myocytes in vitro. Toxicol Appl Pharmacol. 2010;249(2):132–9. https://doi.org/10.1016/j.taap.2010.08.026.

Article  CAS  PubMed  Google Scholar 

Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, Doig A, Guilliams T, Latimer J, McNamee C, Norris A, Sanseau P, Cavalla D, Pirmohamed M. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2018;18(1):41–58. https://doi.org/10.1038/nrd.2018.168.

Article  CAS  PubMed  Google Scholar 

Sliwoski G, Kothiwale S, Meiler J, Lowe EW. Computational methods in drug discovery. Pharmacol Rev. 2023;66(1):334–95. https://doi.org/10.1124/pr.112.007336.

Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021;26(1):80–93. https://doi.org/10.1016/j.drudis.2020.10.010.

Article  CAS  PubMed  Google Scholar 

Johnston RC, Yao K, Kaplan Z, Chelliah M, Leswing K, Seekins S, Watts S, Calkins D, et al. Epik: pKa and protonation state prediction through machine learning. J Chem Theory Comput. 2023;19:2380–8.

Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2009;31(2):455–61. https://doi.org/10.1002/jcc.21334.

Berahmand K, Mohammadi M, Faroughi A, Mohammadiani RP. A novel method of spectral clustering in attributed networks by constructing a parameter-free affinity matrix. Cluster Comput. 2021;25(2):869–88. https://doi.org/10.1007/s10586-021-03430-0.

Article  Google Scholar 

Xiong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C, Yin M, Zeng X, Wu C, Lu A, Chen X, Hou T, Cao D. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021;49(W1):W5–14. https://doi.org/10.1093/nar/gkab255.

Borlea ID, Precup RE, Borlea AB. Improvement of K-means cluster quality by post processing resulted clusters. Procedia Comput Sci. 2022;199:63–70. https://doi.org/10.1016/j.procs.2022.01.009.

Article  Google Scholar 

Miyamoto S, Kollman PA. Settle an analytical version of the SHAKE and RATTLE algorithm for rigid water models. J Comput Chem. 1992;13(8):952–62. https://doi.org/10.1002/jcc.540130805.

Article  CAS  Google Scholar 

Sathyamoorthy M, Kuppusamy S, Dhanaraj RK, Ravi V. Improved k-means based q learning- ing algorithm for optimal clustering and node balancing in wsn. Wireless Pers Commun. 2022;122(3):2745–66.

Article  Google Scholar 

Wen J, Zhang Z, Fei L, Zhang B, Xu Y, Zhang Z, Li J. A survey on incomplete multiview clustering. IEEE Trans Syst Man Cybern. 2023;53(2):1136–49. https://doi.org/10.1109/tsmc.2022.3192635.

Abdullah D, Susilo S, Ahmar AS, Rusli R, Hidayat R. The application of K-means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data. Quality Quantity. 2021;56(3):1283–91. https://doi.org/10.1007/s11135-021-01176-w.

Article  PubMed  PubMed Central  Google Scholar 

Li T, Rezaeipanah A, Tag El Din EM. An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement. J King Saud University-Comput Inform Sci. 2022;34(6):3828–42. https://doi.org/10.1016/j.jksuci.2022.04.010.

Article  Google Scholar 

Eminagaoglu M, Oskay RG, Karayigit AI. Evaluation of elemental affinities in coal using agglomerative hierarchical clustering algorithm: a case study in a thick and mineable coal seam (kM2) from Soma Basin (W Turkey). Int J Coal Geol. 2022;259:104045. https://doi.org/10.1016/j.coal.2022.104045.

Article  CAS  Google Scholar 

Wang Y, Ding S, Wang L, Du S. A manifold p-spectral clustering with sparrow search algorithm. Soft Comput. 2022;26(4):1765–77. https://doi.org/10.1007/s00500-022-06741-5

Srivastava PR, Sarkar P, Hanasusanto GA. A robust spectral clustering algorithm for Sub-Gaussian mixture models with outliers. Operations Res. 2023;71(1):224–44. https://doi.org/10.1287/opre.2022.2317.

Danielsson PE. Euclidean distance mapping. Comput Gr Image Process. 1980;14(3):227–48. https://doi.org/10.1016/0146-664x(80)90054-4.

Article  Google Scholar 

Wolber G, Langer T. LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model. 2005;45(1):160–9. https://doi.org/10.1021/ci049885e.

Article  CAS  PubMed  Google Scholar 

Al-Asri J, Fazekas E, Lehoczki G, Perdih A, Görick C, Melzig MF, et al. From carbohydrates to drug-like fragments: Rational development of novel α-amylase inhibitors. Bioorg Med Chem. 2015;23(20):6725–32. https://doi.org/10.1016/j.bmc.2015.09.007.

Hess B, Bekker H, Berendsen HJC, Fraaije JGEM. LINCS: a linear constraint solver for molecular simulations. J Comput Chem. 1997;18(12):1463–72.

Soares TA, Daura X, Oostenbrink C, Smith LJ, van Gunsteren WF. Validation of the GROMOS force-field parameter set 45A3 against nuclear magnetic resonance data of hen egg lysozyme. J Biomol NMR. 2004;30(4):407–22. https://doi.org/10.1007/s10858-004-5430-1.

Gaussian 16, Revision C.01, Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich AV, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov JF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA, Peralta JE, Ogliaro F, Bearpark MJ, Heyd JJ, Brothers EN, Kudin KN, Staroverov VN, Keith TA, Kobayashi R, Normand J, Raghavachari K, Rendell AP, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ. Gaussian, Inc., Wallingford CT, 2016.

Balajee R, Srinivasadesikan V, Sakthivadivel M, Gunasekaran P. In SilicoScreening, alanine mutation, and DFT approaches for identification of NS2B/NS3 protease inhibitors. Biochem Res Int. 2016;2016:1–13. https://doi.org/10.1155/2016/7264080.

Article  CAS  Google Scholar