{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T01:23:49Z","timestamp":1792373029625,"version":"4.4.0"},"reference-count":74,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>The urgent need to reduce the cost of new drug discovery has led us to create a new, more selective screening method using free chemoinformatics tools to restrict the high failure rates of lead compounds (&gt;90%) during the development process because of the lack of clinical efficacy (40\u201350%), unmanageable toxicity (30%), and poor drug-like properties (10\u201315%). Our efforts focused on new molecular entities (NMEs) with reported activity as tyrosine kinase inhibitors (small molecules) as a class of great potential. The criteria for the new method are acceptable Druglikeness, desirable ADME (absorption, distribution, metabolism, and excretion), and low toxicity. After a bibliographic review, we first selected the 29 most promising compounds, always according to the literature, then collected the in silico calculated data from different platforms, and finally processed them together to conclude at 14 compounds meeting the aforementioned criteria. The novelty of the present screening method is that for the evaluation of the compounds for Druglikeness, and ADMET properties (absorption, distribution, metabolism, excretion, and toxicity), the data of the different platforms were used as a whole, rather than the results of each platform individually. Additionally, we validated our new consensus-based method by comparing the final in silico results with the experimental values of FDA (Food and Drug Administration)-approved tyrosine kinase drugs. Using inferential statistics of 39 FDA-approved tyrosine kinase drugs obtained after applying our method, we delineated the intervals of the desired values of the physicochemical properties of future active compounds. Finally, molecular docking studies enhance the credibility of the applied method as an identification tool of Druglikeness.<\/jats:p>","DOI":"10.3390\/ijms262010207","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T15:20:12Z","timestamp":1760973612000},"page":"10207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Using a Novel Consensus-Based Chemoinformatics Approach to Predict ADMET Properties and Druglikeness of Tyrosine Kinase Inhibitors"],"prefix":"10.3390","volume":"26","author":[{"given":"Evangelos","family":"Mavridis","sequence":"first","affiliation":[{"name":"Laboratory of Pharmaceutical Chemistry, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1844-350X","authenticated-orcid":false,"given":"Dimitra","family":"Hadjipavlou-Litina","sequence":"additional","affiliation":[{"name":"Laboratory of Pharmaceutical Chemistry, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/S0022-2836(02)00538-7","article-title":"Sequence and structure classification of kinases","volume":"320","author":"Cheek","year":"2002","journal-title":"J. Mol. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/nrd.2018.21","article-title":"Kinase inhibitors: The road ahead","volume":"17","author":"Ferguson","year":"2018","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1124\/jpet.105.084145","article-title":"Role of tyrosine kinase inhibitors in cancer therapy","volume":"315","author":"Arora","year":"2005","journal-title":"J. Pharmacol. Exp. Ther."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13045-020-00977-0","article-title":"Tyrosine kinase inhibitors for solid tumors in the past 20 years (2001\u20132020)","volume":"13","author":"Huang","year":"2020","journal-title":"J. Hematol. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3867","DOI":"10.1021\/jm000292e","article-title":"Prediction of drug absorption using multivariate statistics","volume":"43","author":"Egan","year":"2000","journal-title":"J. Med. Chem."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3049","DOI":"10.1016\/j.apsb.2022.02.002","article-title":"Why 90% of clinical drug development fails and how to improve it?","volume":"12","author":"Sun","year":"2022","journal-title":"Acta Pharm. Sin. B"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1038\/nchem.1243","article-title":"Quantifying the chemical beauty of drugs","volume":"4","author":"Bickerton","year":"2012","journal-title":"Nat. Chem."},{"key":"ref_8","unstructured":"(2025, January 24). Available online: https:\/\/www.molinspiration.com\/."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1002\/jcc.540150503","article-title":"ICM\u2014A New Method for Protein Modeling and Design: Applications to Docking and Structure Prediction from the Distorted Native Conformation","volume":"15","author":"Abagyan","year":"1994","journal-title":"J. Comput. Chem."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Daina, A., Michielin, O., and Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 7.","DOI":"10.1038\/srep42717"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"P17","DOI":"10.1186\/1758-2946-4-S1-P17","article-title":"http:\/\/Mcule.com: A public web service for drug discovery","volume":"4","author":"Kiss","year":"2012","journal-title":"J. Cheminform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s13321-018-0283-x","article-title":"ADMETlab: A platform for systematic ADMET evaluation based on a comprehensively collected ADMET database","volume":"10","author":"Dong","year":"2018","journal-title":"J. Cheminform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"W5","DOI":"10.1093\/nar\/gkab255","article-title":"ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties","volume":"49","author":"Xiong","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4066","DOI":"10.1021\/acs.jmedchem.5b00104","article-title":"pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures","volume":"58","author":"Pires","year":"2015","journal-title":"J. Med. Chem."},{"key":"ref_15","unstructured":"(2025, January 24). Available online: https:\/\/biosig.lab.uq.edu.au\/deeppk\/."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1093\/bioinformatics\/bty707","article-title":"admetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties","volume":"35","author":"Yang","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3099","DOI":"10.1021\/ci300367a","article-title":"AdmetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties","volume":"52","author":"Cheng","year":"2012","journal-title":"J. Chem. Inf. Model."},{"key":"ref_18","first-page":"5","article-title":"The PreADME: PC-based program for batch prediction of ADME properties","volume":"9","author":"Lee","year":"2004","journal-title":"EuroQSAR"},{"key":"ref_19","unstructured":"Martin, T.M. (2020). Toxicity Estimation Software Tool."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0169-409X(96)00423-1","article-title":"Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings","volume":"23","author":"Lipinski","year":"1997","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1021\/cc9800071","article-title":"A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases","volume":"1","author":"Ghose","year":"1999","journal-title":"J. Comb. Chem."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.1021\/jm020017n","article-title":"Molecular properties that influence the oral bioavailability of drug candidates","volume":"45","author":"Veber","year":"2002","journal-title":"J. Med. Chem."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1021\/jm015507e","article-title":"Simple selection criteria for drug-like chemical matter","volume":"44","author":"Muegge","year":"2001","journal-title":"J. Med. Chem."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1023\/A:1008130001697","article-title":"Property distribution of drug-related chemical databases","volume":"14","author":"Oprea","year":"2000","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3743","DOI":"10.1002\/(SICI)1521-3773(19991216)38:24<3743::AID-ANIE3743>3.0.CO;2-U","article-title":"The design of leadlike combinatorial libraries","volume":"38","author":"Teague","year":"1999","journal-title":"Angew. Chem.\u2014Int. Ed."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1021\/jm701122q","article-title":"Generation of a set of simple, interpretable ADMET rules of thumb","volume":"51","author":"Gleeson","year":"2008","journal-title":"J. Med. Chem."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1021\/acschembio.7b00903","article-title":"Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017\u2014Utility and Limitations","volume":"13","author":"Baell","year":"2018","journal-title":"ACS Chem. Biol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1002\/cmdc.200700139","article-title":"Lessons learnt from assembling screening libraries for drug discovery for neglected diseases","volume":"3","author":"Brenk","year":"2008","journal-title":"ChemMedChem"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.ejmech.2016.01.039","article-title":"The discovery of 2-substituted phenol quinazolines as potent RET kinase inhibitors with improved KDR selectivity","volume":"112","author":"Newton","year":"2016","journal-title":"Eur. J. Med. Chem."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1016\/j.bioorg.2018.09.001","article-title":"Identification of new pyrrolo [2,3-d]pyrimidines as potent VEGFR-2 tyrosine kinase inhibitors: Design, synthesis, biological evaluation and molecular modeling","volume":"81","author":"Adel","year":"2018","journal-title":"Bioorg. Chem."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"El-Metwally, S.A., Abou-El-Regal, M.M., Eissa, I.H., Mehany, A.B., Mahdy, H.A., Elkady, H., Elwan, A., and Elkaeed, E.B. (2021). Discovery of thieno [2,3-d]pyrimidine-based derivatives as potent VEGFR-2 kinase inhibitors and anti-cancer agents. Bioorg. Chem., 112.","DOI":"10.1016\/j.bioorg.2021.104947"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1016\/j.bmcl.2015.02.002","article-title":"Identification and optimization of pyridazinones as potent and selective c-Met kinase inhibitors","volume":"25","author":"Dorsch","year":"2015","journal-title":"Bioorg. Med. Chem. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"18872","DOI":"10.1021\/acsomega.0c02038","article-title":"Dual Targeting of VEGFR2 and C-Met Kinases via the Design and Synthesis of Substituted 3-(Triazolo-thiadiazin-3-yl)indolin-2-one Derivatives as Angiogenesis Inhibitors","volume":"5","author":"Mohamady","year":"2020","journal-title":"ACS Omega"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Elmetwally, S.A., Saied, K.F., Eissa, I.H., and Elkaeed, E.B. (2019). Design, synthesis and anticancer evaluation of thieno [2,3-d]pyrimidine derivatives as dual EGFR\/HER2 inhibitors and apoptosis inducers. Bioorg. Chem., 88.","DOI":"10.1016\/j.bioorg.2019.102944"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5112","DOI":"10.1021\/jm4017762","article-title":"Discovery of a series of 2,5-diaminopyrimidine covalent irreversible inhibitors of Bruton\u2019s tyrosine kinase with in vivo antitumor activity","volume":"57","author":"Li","year":"2014","journal-title":"J. Med. Chem."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sherbiny, F.F., Bayoumi, A.H., El-Morsy, A.M., Sobhy, M., and Hagras, M. (2021). Design, Synthesis, biological Evaluation, and molecular docking studies of novel Pyrazolo [3,4-d]Pyrimidine derivative scaffolds as potent EGFR inhibitors and cell apoptosis inducers. Bioorg. Chem., 116.","DOI":"10.1016\/j.bioorg.2021.105325"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1016\/j.bmc.2018.03.017","article-title":"Design, synthesis and biological evaluation of novel 3-substituted pyrazolopyrimidine derivatives as potent Bruton\u2019s tyrosine kinase (BTK) inhibitors","volume":"26","author":"Zheng","year":"2018","journal-title":"Bioorg. Med. Chem."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"17881","DOI":"10.1021\/acsomega.2c01188","article-title":"Discovery of Anilino-1,4-naphthoquinones as Potent EGFR Tyrosine Kinase Inhibitors: Synthesis, Biological Evaluation, and Comprehensive Molecular Modeling","volume":"7","author":"Mahalapbutr","year":"2022","journal-title":"ACS Omega"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ciaffaglione, V., Consoli, V., Intagliata, S., Marrazzo, A., Romeo, G., Pittal\u00e0, V., Greish, K., Vanella, L., Floresta, G., and Rescifina, A. (2022). Novel Tyrosine Kinase Inhibitors to Target Chronic Myeloid Leukemia. Molecules, 27.","DOI":"10.3390\/molecules27103220"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113222","DOI":"10.1016\/j.ejmech.2021.113222","article-title":"Pyrazolo [3,4-d]pyrimidine-based dual EGFR T790M\/HER2 inhibitors: Design, synthesis, structure\u2013activity relationship and biological activity as potential antitumor and anticonvulsant agents","volume":"214","author":"Lamie","year":"2021","journal-title":"Eur. J. Med. Chem."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Farghaly, A.M., AboulWafa, O.M., Baghdadi, H.H., El Razik, H.A.A., Sedra, S.M., and Shamaa, M.M. (2021). New thieno [3,2-d]pyrimidine-based derivatives: Design, synthesis and biological evaluation as antiproliferative agents, EGFR and ARO inhibitors inducing apoptosis in breast cancer cells. Bioorg. Chem., 115.","DOI":"10.1016\/j.bioorg.2021.105208"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e2000219","DOI":"10.1002\/ardp.202000219","article-title":"N-Substituted-4-phenylphthalazin-1-amine-derived VEGFR-2 inhibitors: Design, synthesis, molecular docking, and anticancer evaluation studies","volume":"354","author":"Ibrahim","year":"2021","journal-title":"Arch. Pharm."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1700240","DOI":"10.1002\/ardp.201700240","article-title":"Design, Synthesis, Molecular Docking, and Anticancer Activity of Phthalazine Derivatives as VEGFR-2 Inhibitors","volume":"350","author":"Ayyad","year":"2017","journal-title":"Arch. Pharm."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Alanazi, M.M., Mahdy, H.A., Alsaif, N.A., Obaidullah, A.J., Alkahtani, H.M., Al-Mehizia, A.A., Alsubaie, S.M., Dahab, M.A., and Eissa, I.H. (2021). New bis([1,2,4]triazolo)[4,3-a:3\u2032,4\u2032-c]quinoxaline derivatives as VEGFR-2 inhibitors and apoptosis inducers: Design, synthesis, in silico studies, and anticancer evaluation. Bioorg. Chem., 112.","DOI":"10.1016\/j.bioorg.2021.104949"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Saleh, N.M., El-Gaby, M.S., El-Adl, K., and El-Sattar, N.E.A. (2020). Design, green synthesis, molecular docking and anticancer evaluations of diazepam bearing sulfonamide moieties as VEGFR-2 inhibitors. Bioorg. Chem., 104.","DOI":"10.1016\/j.bioorg.2020.104350"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ahmed, E.Y., Latif, N.A.A., El-Mansy, M.F., Elserwy, W.S., and Abdelhafez, O.M. (2020). VEGFR-2 inhibiting effect and molecular modeling of newly synthesized coumarin derivatives as anti-breast cancer agents. Bioorg. Med. Chem., 28.","DOI":"10.1016\/j.bmc.2020.115328"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e1900340","DOI":"10.1002\/ardp.201900340","article-title":"Design, synthesis, and molecular docking of novel 2-arylbenzothiazole multiangiokinase inhibitors targeting breast cancer","volume":"353","author":"Mahmoud","year":"2020","journal-title":"Arch. Pharm."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"AboulWafa, O.M., Daabees, H.M., and Badawi, W.A. (2020). 2-Anilinopyrimidine derivatives: Design, synthesis, in vitro anti-proliferative activity, EGFR and ARO inhibitory activity, cell cycle analysis and molecular docking study. Bioorg. Chem., 99.","DOI":"10.1016\/j.bioorg.2020.103798"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"12526","DOI":"10.1021\/acs.jmedchem.0c00702","article-title":"Discovery of BIIB068: A Selective, Potent, Reversible Bruton\u2019s Tyrosine Kinase Inhibitor as an Orally Efficacious Agent for Autoimmune Diseases","volume":"63","author":"Ma","year":"2020","journal-title":"J. Med. Chem."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1038\/s41587-019-0224-x","article-title":"Deep learning enables rapid identification of potent DDR1 kinase inhibitors","volume":"37","author":"Zhavoronkov","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.bmcl.2018.11.058","article-title":"Discovery of potent and selective 5-azaindazole inhibitors of leucine-rich repeat kinase 2 (LRRK2)\u2014Part 1","volume":"29","author":"Osborne","year":"2019","journal-title":"Bioorg. Med. Chem. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"107059","DOI":"10.1016\/j.phrs.2024.107059","article-title":"Properties of FDA-approved small molecule protein kinase inhibitors: A 2024 update","volume":"200","author":"Roskoski","year":"2024","journal-title":"Pharmacol. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s40264-013-0047-5","article-title":"Cardiovascular safety of tyrosine kinase inhibitors: With a special focus on cardiac repolarisation (QT Interval)","volume":"36","author":"Shah","year":"2013","journal-title":"Drug Saf."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Dulsat, J., L\u00f3pez-Nieto, B., Estrada-Tejedor, R., and Borrell, J.I. (2023). Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments. Molecules, 28.","DOI":"10.3390\/molecules28020776"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Vigan\u00f2, M., La Milia, M., Grassini, M.V., Pugliese, N., De Giorgio, M., and Fagiuoli, S. (2023). Hepatotoxicity of Small Molecule Protein Kinase Inhibitors for Cancer. Cancers, 15.","DOI":"10.3390\/cancers15061766"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"D1265","DOI":"10.1093\/nar\/gkad976","article-title":"DrugBank 6.0: The DrugBank Knowledgebase for 2024","volume":"52","author":"Knox","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"ref_57","unstructured":"U.S. Department of Health and Human Services Food and Drug Administration (2024, September 15). Available online: https:\/\/open.fda.gov\/fdalabels\/active_ingredient\/."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Al-Sanea, M.M., Chilingaryan, G., Abelyan, N., Sargsyan, A., Hovhannisyan, S., Gasparyan, H., Gevorgyan, S., Albogami, S., Ghoneim, M.M., and Farag, A.K. (2021). Identification of novel potential vegfr-2 inhibitors using a combination of computational methods for drug discovery. Life, 11.","DOI":"10.3390\/life11101070"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5712","DOI":"10.1080\/07391102.2021.1872417","article-title":"Exploration of structural requirements for the inhibition of VEGFR-2 tyrosine kinase: Binding site analysis of type II, \u2018DFG-out\u2019 inhibitors","volume":"40","author":"Modi","year":"2022","journal-title":"J. Biomol. Struct. Dyn."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1038\/s43018-022-00412-y","article-title":"Discovery of potent and selective HER2 inhibitors with efficacy against HER2 exon 20 insertion-driven tumors, which preserve wild-type EGFR signaling","volume":"3","author":"Wilding","year":"2022","journal-title":"Nat. Cancer"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"18756","DOI":"10.1074\/jbc.M110.206193","article-title":"Structural analysis of the mechanism of inhibition and allosteric activation of the kinase domain of HER2 protein","volume":"286","author":"Aertgeerts","year":"2011","journal-title":"J. Biol. Chem."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Huang, W.-S., Li, F., Gong, Y., Zhang, Y., Youngsaye, W., Xu, Y., Zhu, X., Greenfield, M.T., Kohlmann, A., and Taslimi, P.M. (2023). Discovery of mobocertinib, a potent, oral inhibitor of EGFR exon 20 insertion mutations in non\u2013small cell lung cancer. Bioorg. Med. Chem. Lett., 80.","DOI":"10.1016\/j.bmcl.2022.129084"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1007\/s00044-022-02952-5","article-title":"Discovery of mobocertinib, a new irreversible tyrosine kinase inhibitor indicated for the treatment of non-small-cell lung cancer harboring EGFR exon 20 insertion mutations","volume":"31","author":"Wang","year":"2022","journal-title":"Med. Chem. Res."},{"key":"ref_64","first-page":"1000","article-title":"Validation Through Re-Docking, Cross-Docking and Ligand Enrichment in Various Well-Resoluted Mao-B Receptors","volume":"13","author":"Mateev","year":"2021","journal-title":"Artic. Int. J. Pharm. Sci. Res."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ram\u00edrez, D., and Caballero, J. (2018). Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data?. Molecules, 23.","DOI":"10.3390\/molecules23051038"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Maltarollo, V.G. (2024). Computer-Aided and Machine Learning-Driven Drug Design. From Theory to Applications, Springer. Computer-Aided Drug Discovery and Design 3.","DOI":"10.1007\/978-3-031-76718-0_1"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1021\/acs.jpcb.3c06662","article-title":"OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials","volume":"128","author":"Eastman","year":"2024","journal-title":"J. Phys. Chem. B"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/s13321-019-0358-3","article-title":"Gypsum-DL: An open-source program for preparing small-molecule libraries for structure-based virtual screening","volume":"11","author":"Ropp","year":"2019","journal-title":"J. Cheminform."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s13321-021-00522-2","article-title":"GNINA 1.0: Molecular docking with deep learning","volume":"13","author":"McNutt","year":"2021","journal-title":"J. Cheminform."},{"key":"ref_70","unstructured":"(The PyMOL Molecular Graphics System, 2025). The PyMOL Molecular Graphics System, Version 3.0.4."},{"key":"ref_71","unstructured":"(Schr\u00f6dinger Release 2025-3: Maestro, 2025). Schr\u00f6dinger Release 2025-3: Maestro, Version 14.5.131."},{"key":"ref_72","unstructured":"(2025, September 14). Available online: https:\/\/github.com\/InformaticsMatters\/docking-validation\/tree\/7ca594d166fcf6f59fd9501ef1e39efa71c4cb60\/datasets\/DEKOIS_2.0."},{"key":"ref_73","unstructured":"IBM Corp. (IBM SPSS Statistics for Windows, 2023). IBM SPSS Statistics for Windows, Version 29.0.2.0."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4350","DOI":"10.1021\/jm020155c","article-title":"Do structurally similar molecules have similar biological activity?","volume":"45","author":"Martin","year":"2002","journal-title":"J. Med. Chem."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/20\/10207\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T04:27:49Z","timestamp":1761107269000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/20\/10207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,20]]},"references-count":74,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["ijms262010207"],"URL":"https:\/\/doi.org\/10.3390\/ijms262010207","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,20]]}}}