Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: a cancer. J Clin. 2024;74(3):229–63. https://doi.org/10.3322/caac.21834.

Article  Google Scholar 

Urbach D, Lupien M, Karagas MR, Moore JH. Cancer heterogeneity: origins and implications for genetic association studies. Trends Genet. 2012;28(11):538–43. https://doi.org/10.1016/j.tig.2012.07.001.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ornitz DM, Itoh N. The fibroblast growth factor signaling pathway. WIREs Dev Biol. 2015;4(3):215–66. https://doi.org/10.1002/wdev.176.

Article  CAS  Google Scholar 

Perez-Garcia J, Muñoz-Couselo E, Soberino J, Racca F, Cortes J. Targeting FGFR pathway in breast cancer. Breast. 2018;37(2018):126–33. https://doi.org/10.1016/j.breast.2017.10.014.

Article  CAS  PubMed  Google Scholar 

Liu Q, Huang J, Yan W, Liu Z, Liu S, Fang W. FGFR families: biological functions and therapeutic interventions in tumors. MedComm. 2023;4(5):e367. https://doi.org/10.1002/mco2.367.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Turner N, Pearson A, Sharpe R, Lambros M, Geyer F, Lopez-Garcia MA, Natrajan R, Marchio C, Iorns E, Mackay A, Gillett C, Grigoriadis A, Tutt A, Reis-Filho JS, Ashworth A. FGFR1 amplification drives endocrine therapy resistance and is a therapeutic target in breast cancer. Cancer Res. 2010;70(5):2085–94. https://doi.org/10.1158/0008-5472.CAN-09-3746.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wang K, Ji W, Yu Y, Li Z, Niu X, Xia W, et al. FGFR1-ERK1/2-SOX2 axis promotes cell proliferation, epithelial–mesenchymal transition, and metastasis in FGFR1-amplified lung cancer. Oncogene. 2018;37(39):5340–54. https://doi.org/10.1038/s41388-018-0311-3.

Article  CAS  PubMed  Google Scholar 

Gorringe KL, Jacobs S, Thompson ER, Sridhar A, Qiu W, Choong DYH, et al. High-resolution single nucleotide polymorphism array analysis of epithelial ovarian cancer reveals numerous microdeletions and amplifications. Clin Cancer Res. 2007;13(16):4731–9. https://doi.org/10.1158/1078-0432.CCR-07-0502.

Article  CAS  PubMed  Google Scholar 

Ross JS, Wang K, Al-Rohil RN, Nazeer T, Sheehan CE, Otto GA, et al. Advanced urothelial carcinoma: next-generation sequencing reveals diverse genomic alterations and targets of therapy. Mod Pathol. 2014;27(2):271–80. https://doi.org/10.1038/modpathol.2013.135.

Article  CAS  PubMed  Google Scholar 

Edwards J, Krishna NS, Witton CJ, Bartlett JMS. Gene amplifications associated with the development of hormone-resistant prostate cancer. Clin Cancer Res. 2003;9(14):5271–81.

CAS  PubMed  Google Scholar 

Schäfer MH, Lingohr P, Sträßer A, Lehnen NC, Braun M, Perner S, Höller T, Kristiansen G, Kalff JC, Gütgemann I. Fibroblast growth factor receptor 1 gene amplification in gastric adenocarcinoma. Hum Pathol. 2015;46(10):1488–95. https://doi.org/10.1016/j.humpath.2015.06.007.

Article  CAS  PubMed  Google Scholar 

Katoh M. Therapeutics targeting FGF signaling network in human diseases. Trends Pharmacol Sci. 2016;37(12):1081–96. https://doi.org/10.1016/j.tips.2016.10.003.

Article  CAS  PubMed  Google Scholar 

Zhang P, Yue L, Leng Q, Chang C, Gan C, Ye T, et al. Targeting FGFR for cancer therapy. J Hematol Oncol. 2024;17(1):39. https://doi.org/10.1186/s13045-024-01558-1.

Article  PubMed  PubMed Central  Google Scholar 

Touat M, Ileana E, Postel-Vinay S, André F, Soria JC. Targeting FGFR signaling in cancer. Clin Cancer Res. 2015;21(12):2684–94. https://doi.org/10.1158/1078-0432.CCR-14-2329.

Article  CAS  PubMed  Google Scholar 

Du S, Zhang Y, Xu J. Current progress in cancer treatment by targeting FGFR signaling. Cancer Biol Med. 2023;20(7):490–9. https://doi.org/10.20892/j.issn.2095-3941.2023.0137.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Facchinetti F, Hollebecque A, Braye F, Vasseur D, Pradat Y, Bahleda R, et al. Resistance to selective FGFR inhibitors in FGFR-driven urothelial cancer. Cancer Discov. 2023;13(9):1998–2011. https://doi.org/10.1158/2159-8290.CD-22-1441.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hinkson IV, Madej B, Stahlberg EA. Accelerating therapeutics for opportunities in medicine: a paradigm shift in drug discovery. Front Pharmacol. 2020;11:770. https://doi.org/10.3389/fphar.2020.00770.

Article  PubMed  PubMed Central  Google Scholar 

Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov. 2010;9(3):203–14. https://doi.org/10.1038/nrd3078.

Article  CAS  PubMed  Google Scholar 

Yu W, MacKerell AD. (2017) Computer-Aided Drug Design Methods, in: P. Sass, editor, Antibiotics: Methods and Protocols. Springer, New York, NY, 2017: 85–106. https://doi.org/10.1007/978-1-4939-6634-9_5

Sydow D, Morger A, Driller M, Volkamer A. TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data. J Cheminform. 2019;11:29. https://doi.org/10.1186/s13321-019-0351-x.

Article  PubMed  PubMed Central  Google Scholar 

Poudel Chhetri S, Bhandari VS, Maharjan R, Lamichhane TR. Identification of lead inhibitors for 3CLpro of SARS-CoV-2 target using machine learning based virtual screening, ADMET analysis, molecular docking and molecular dynamics simulations. RSC Adv. 2024;14(40):29683–92. https://doi.org/10.1039/D4RA04502E.

Article  Google Scholar 

Salimi A, Lim JH, Jang JH, Lee JY. The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors. Sci Rep. 2022;12(1):18825. https://doi.org/10.1038/s41598-022-22992-6.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow RA, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M, Hill JE, Krutoholow E, Kohler M, Blaney J, Funatsu K, Luebkemann C, Schneider G. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov. 2020;19(5):353–64. https://doi.org/10.1038/s41573-019-0050-3.

Article  CAS  PubMed  Google Scholar 

Prašnikar E, Ljubič M, Perdih A, Borišek J. Machine learning heralding a new development phase in molecular dynamics simulations. Artif Intell Rev. 2024;57(4):102. https://doi.org/10.1007/s10462-024-10731-4.

Article  Google Scholar 

Caulfield T, Islam N, Coban M, Fuller J, Weber C, Chitale R, Jussila B, Brock T, Tao C. (2025) Dynamicasome! Comprehensive mutational analysis and AI-Driven prediction of PMM2 pathogenicity: integrating molecular dynamics simulations with Machine Learning Models. https://doi.org/10.21203/rs.3.rs-5538161/v1

Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2023 update. Nucleic Acids Res. 2023;51(D1):D1373–80. https://doi.org/10.1093/nar/gkac956.

Article  PubMed  PubMed Central  Google Scholar 

Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform. 2012;4:17. https://doi.org/10.1186/1758-2946-4-17.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. Schwede T, SWISS-MODEL: homology modelling of protein structures and complexes. Nuc Acids Res. 2018;46(W1):W296–303. https://doi.org/10.1093/nar/gky427.

Article  CAS  Google Scholar 

Sharma BP, Adhikari Subin J, Marasini BP, Adhikari R, Pandey SK, Sharma ML. Triazole based Schiff bases and their oxovanadium(IV) complexes: synthesis, characterization, antibacterial assay, and computational assessments. Heliyon. 2023. https://doi.org/10.1016/j.heliyon.2023.e15239.

Article