{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T11:16:59Z","timestamp":1769080619927,"version":"3.49.0"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T00:00:00Z","timestamp":1744675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"DOI":"10.1186\/s13321-025-00998-2","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T09:52:11Z","timestamp":1744710731000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery"],"prefix":"10.1186","volume":"17","author":[{"given":"Maria","family":"Zavadskaya","sequence":"first","affiliation":[]},{"given":"Anastasia","family":"Orlova","sequence":"additional","affiliation":[]},{"given":"Andrei","family":"Dmitrenko","sequence":"additional","affiliation":[]},{"given":"Vladimir","family":"Vinogradov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"key":"998_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/iid3.934","volume":"11","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Zhang Y, Yu W et al (2023) Immunomodulatory role of spleen tyrosine kinase in chronic inflammatory and autoimmune diseases. Immun Inflamm Dis 11:e934. https:\/\/doi.org\/10.1002\/iid3.934","journal-title":"Immun Inflamm Dis"},{"key":"998_CR2","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1186\/ar3198","volume":"12","author":"ON Pamuk","year":"2010","unstructured":"Pamuk ON, Tsokos GC (2010) Spleen tyrosine kinase inhibition in the treatment of autoimmune, allergic and autoinflammatory diseases. Arthritis Res Ther 12:222. https:\/\/doi.org\/10.1186\/ar3198","journal-title":"Arthritis Res Ther"},{"key":"998_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.bmc.2023.117514","volume":"96","author":"Z Wang","year":"2023","unstructured":"Wang Z, Qu S, Yuan J et al (2023) Review and prospects of targeted therapies for spleen tyrosine kinase (SYK). Bioorg Med Chem 96:117514. https:\/\/doi.org\/10.1016\/j.bmc.2023.117514","journal-title":"Bioorg Med Chem"},{"key":"998_CR4","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1038\/nri2765","volume":"10","author":"A M\u00f3csai","year":"2010","unstructured":"M\u00f3csai A, Ruland J, Tybulewicz VLJ (2010) The SYK tyrosine kinase: a crucial player in diverse biological functions. Nat Rev Immunol 10:387\u2013402. https:\/\/doi.org\/10.1038\/nri2765","journal-title":"Nat Rev Immunol"},{"key":"998_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e15625","volume":"9","author":"M Li","year":"2023","unstructured":"Li M, Wang P, Zou Y et al (2023) Spleen tyrosine kinase (SYK) signals are implicated in cardio-cerebrovascular diseases. Heliyon 9:e15625. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e15625","journal-title":"Heliyon"},{"key":"998_CR6","doi-asserted-by":"publisher","DOI":"10.1053\/j.seminhematol.2024.04.002","author":"JT Patton","year":"2024","unstructured":"Patton JT, Woyach JA (2024) Targeting the B cell receptor signaling pathway in chronic lymphocytic leukemia. Semin Hematol. https:\/\/doi.org\/10.1053\/j.seminhematol.2024.04.002","journal-title":"Semin Hematol"},{"key":"998_CR7","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1182\/blood-2020-141045","volume":"136","author":"W Dummer","year":"2020","unstructured":"Dummer W, Markovtsov VV, Tong S et al (2020) Clinical trial to evaluate an approved ITP therapy targeting spleen tyrosine kinase (SYK) for prevention and treatment of COVID-19 related complications. Blood 136:35. https:\/\/doi.org\/10.1182\/blood-2020-141045","journal-title":"Blood"},{"key":"998_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/s13045-017-0512-1","author":"D Liu","year":"2017","unstructured":"Liu D, Mamorska-Dyga A (2017) Syk inhibitors in clinical development for hematological malignancies. J Hematol Oncol. https:\/\/doi.org\/10.1186\/s13045-017-0512-1","journal-title":"J Hematol Oncol"},{"key":"998_CR9","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1111\/bjh.19078","volume":"203","author":"H Al-Samkari","year":"2023","unstructured":"Al-Samkari H, Neufeld EJ (2023) Novel therapeutics and future directions for refractory immune thrombocytopenia. Br J Haematol 203:65\u201378. https:\/\/doi.org\/10.1111\/bjh.19078","journal-title":"Br J Haematol"},{"key":"998_CR10","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13045-023-01401-z","volume":"16","author":"XG Liu","year":"2023","unstructured":"Liu XG, Hou Y, Hou M (2023) How we treat primary immune thrombocytopenia in adults. J Hematol Oncol 16:4. https:\/\/doi.org\/10.1186\/s13045-023-01401-z","journal-title":"J Hematol Oncol"},{"key":"998_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.taap.2024.116911","volume":"485","author":"NHC Loos","year":"2024","unstructured":"Loos NHC, Sparidans RW, Heydari P et al (2024) The ABCB1 and ABCG2 efflux transporters limit brain disposition of the SYK inhibitors entospletinib and lanraplenib. Toxicol Appl Pharmacol 485:116911. https:\/\/doi.org\/10.1016\/j.taap.2024.116911","journal-title":"Toxicol Appl Pharmacol"},{"key":"998_CR12","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1002\/art.30114","volume":"63","author":"MC Genovese","year":"2011","unstructured":"Genovese MC, Kavanaugh A, Weinblatt ME et al (2011) An oral Syk kinase inhibitor in the treatment of rheumatoid arthritis: a three-month randomized, placebo-controlled, phase II study in patients with active rheumatoid arthritis that did not respond to biologic agents. Arthritis Rheum 63:337\u2013345. https:\/\/doi.org\/10.1002\/art.30114","journal-title":"Arthritis Rheum"},{"key":"998_CR13","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1007\/s40265-021-01524-y","volume":"81","author":"J Paik","year":"2021","unstructured":"Paik J (2021) Fostamatinib: a review in chronic immune thrombocytopenia. Drugs 81:935\u2013943. https:\/\/doi.org\/10.1007\/s40265-021-01524-y","journal-title":"Drugs"},{"key":"998_CR14","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3138\/cim-2024-2569","volume":"47","author":"J Britto","year":"2024","unstructured":"Britto J, Holbrook A, Sun H et al (2024) Thrombopoietin receptor agonists and other second-line therapies for immune thrombocytopenia: a narrative review with a focus on drug access in Canada. Clin Invest Med 47:13\u201322. https:\/\/doi.org\/10.3138\/cim-2024-2569","journal-title":"Clin Invest Med"},{"key":"998_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-024-10714-5","volume":"57","author":"AC Pushkaran","year":"2024","unstructured":"Pushkaran AC, Arabi AA (2024) From understanding diseases to drug design: can artificial intelligence bridge the gap? Artif Intell Rev 57:1\u201339. https:\/\/doi.org\/10.1007\/s10462-024-10714-5","journal-title":"Artif Intell Rev"},{"key":"998_CR16","doi-asserted-by":"publisher","first-page":"1676","DOI":"10.3390\/ijms22041676","volume":"22","author":"VD Mouchlis","year":"2021","unstructured":"Mouchlis VD, Afantitis A, Serra A et al (2021) Advances in de novo drug design: from conventional to machine learning methods. Int J Mol Sci 22:1676. https:\/\/doi.org\/10.3390\/ijms22041676","journal-title":"Int J Mol Sci"},{"key":"998_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2024.100576","volume":"17","author":"G Obaido","year":"2024","unstructured":"Obaido G, Mienye ID, Egbelowo OF et al (2024) Supervised machine learning in drug discovery and development: algorithms, applications, challenges, and prospects. Mach Learn Appl 17:100576. https:\/\/doi.org\/10.1016\/j.mlwa.2024.100576","journal-title":"Mach Learn Appl"},{"key":"998_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.antiviral.2023.105740","volume":"220","author":"PP Parvatikar","year":"2023","unstructured":"Parvatikar PP, Patil S, Khaparkhuntikar K et al (2023) Artificial intelligence: machine learning approach for screening large database and drug discovery. Antiviral Res 220:105740. https:\/\/doi.org\/10.1016\/j.antiviral.2023.105740","journal-title":"Antiviral Res"},{"key":"998_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2024.103992","volume":"29","author":"A Gangwal","year":"2024","unstructured":"Gangwal A, Lavecchia A (2024) Unleashing the power of generative AI in drug discovery. Drug Discov Today 29:103992. https:\/\/doi.org\/10.1016\/j.drudis.2024.103992","journal-title":"Drug Discov Today"},{"key":"998_CR20","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1038\/s41573-023-00832-0","volume":"23","author":"A Tropsha","year":"2023","unstructured":"Tropsha A, Isayev O, Varnek A et al (2023) Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 23:141\u2013155. https:\/\/doi.org\/10.1038\/s41573-023-00832-0","journal-title":"Nat Rev Drug Discov"},{"key":"998_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.molstruc.2022.132531","volume":"1257","author":"GSP Matada","year":"2022","unstructured":"Matada GSP, Dhiwar PS, Abbas N et al (2022) Pharmacophore modeling, virtual screening, molecular docking and dynamics studies for the discovery of HER2-tyrosine kinase inhibitors: an in-silico approach. J Mol Struct 1257:132531. https:\/\/doi.org\/10.1016\/j.molstruc.2022.132531","journal-title":"J Mol Struct"},{"key":"998_CR22","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1468","volume":"10","author":"D Schaller","year":"2020","unstructured":"Schaller D, \u0160ribar D, Noonan T et al (2020) Next generation 3D pharmacophore modeling. Wiley Interdiscip Rev Comput Mol Sci 10:e1468. https:\/\/doi.org\/10.1002\/wcms.1468","journal-title":"Wiley Interdiscip Rev Comput Mol Sci"},{"key":"998_CR23","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.2174\/0113895575258033231024073521","volume":"24","author":"SMA Kawsar","year":"2023","unstructured":"Kawsar SMA, Munia NS, Saha S et al (2023) In silico pharmacokinetics, molecular docking and molecular dynamics simulation studies of nucleoside analogs for drug discovery: a mini review. Mini Rev Med Chem 24:1070\u20131088. https:\/\/doi.org\/10.2174\/0113895575258033231024073521","journal-title":"Mini Rev Med Chem"},{"key":"998_CR24","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1038\/s41586-023-05905-z","volume":"616","author":"AV Sadybekov","year":"2023","unstructured":"Sadybekov AV, Katritch V (2023) Computational approaches streamlining drug discovery. Nature 616:673\u2013685. https:\/\/doi.org\/10.1038\/s41586-023-05905-z","journal-title":"Nature"},{"key":"998_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbi.2023.102575","volume":"80","author":"JP Janet","year":"2023","unstructured":"Janet JP, Mervin L, Engkvist O et al (2023) Artificial intelligence in molecular de novo design: integration with experiment. Curr Opin Struct Biol 80:102575. https:\/\/doi.org\/10.1016\/j.sbi.2023.102575","journal-title":"Curr Opin Struct Biol"},{"key":"998_CR26","doi-asserted-by":"publisher","first-page":"2174","DOI":"10.1021\/acs.jcim.3c01496","volume":"64","author":"C Pang","year":"2024","unstructured":"Pang C, Qiao J, Zeng X et al (2024) Deep generative models in de novo drug molecule generation. J Chem Inf Model 64:2174\u20132194. https:\/\/doi.org\/10.1021\/acs.jcim.3c01496","journal-title":"J Chem Inf Model"},{"key":"998_CR27","doi-asserted-by":"publisher","first-page":"851","DOI":"10.2174\/0115748936276510231123121404","volume":"19","author":"JS Mathivanan","year":"2024","unstructured":"Mathivanan JS, Dhayabaran VV, David MR et al (2024) Application of deep learning neural networks in computer-aided drug discovery: a review. Curr Bioinform 19:851\u2013858. https:\/\/doi.org\/10.2174\/0115748936276510231123121404","journal-title":"Curr Bioinform"},{"key":"998_CR28","doi-asserted-by":"publisher","first-page":"1331062","DOI":"10.3389\/fphar.2024.1331062","volume":"15","author":"A Gangwal","year":"2024","unstructured":"Gangwal A, Ansari A, Ahmad I et al (2024) Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 15:1331062. https:\/\/doi.org\/10.3389\/fphar.2024.1331062","journal-title":"Front Pharmacol"},{"key":"998_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.copbio.2024.103175","volume":"89","author":"GC Kanakala","year":"2024","unstructured":"Kanakala GC, Devata S, Chatterjee P et al (2024) Generative artificial intelligence for small molecule drug design. Curr Opin Biotechnol 89:103175. https:\/\/doi.org\/10.1016\/j.copbio.2024.103175","journal-title":"Curr Opin Biotechnol"},{"key":"998_CR30","doi-asserted-by":"publisher","first-page":"1886","DOI":"10.1002\/jcc.27354","volume":"45","author":"B Sridharan","year":"2024","unstructured":"Sridharan B, Sinha A, Bardhan J et al (2024) Deep reinforcement learning in chemistry: a review. J Comput Chem 45:1886\u20131898. https:\/\/doi.org\/10.1002\/jcc.27354","journal-title":"J Comput Chem"},{"key":"998_CR31","doi-asserted-by":"publisher","first-page":"7260","DOI":"10.1021\/acs.jmedchem.4c00091","volume":"67","author":"H Cai","year":"2024","unstructured":"Cai H, Chen W, Jiang J et al (2024) Artificial intelligence-assisted optimization of antipigmentation tyrosinase inhibitors: de novo molecular generation based on a low activity lead compound. J Med Chem 67:7260\u20137275. https:\/\/doi.org\/10.1021\/acs.jmedchem.4c00091","journal-title":"J Med Chem"},{"key":"998_CR32","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2022.909111","volume":"12","author":"V Kumar","year":"2022","unstructured":"Kumar V, Parate S, Danishuddin S et al (2022) 3D-QSAR-based pharmacophore modeling, virtual screening, and molecular dynamics simulations for the identification of spleen tyrosine kinase inhibitors. Front Cell Infect Microbiol 12:909111. https:\/\/doi.org\/10.3389\/fcimb.2022.909111","journal-title":"Front Cell Infect Microbiol"},{"key":"998_CR33","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1080\/1062936x.2023.2266364","volume":"34","author":"S Samanta","year":"2023","unstructured":"Samanta S, Sk MF, Koirala S et al (2023) Exploring molecular interactions of potential inhibitors against the spleen tyrosine kinase implicated in autoimmune disorders via virtual screening and molecular dynamics simulations. SAR QSAR Environ Res 34:869\u2013897. https:\/\/doi.org\/10.1080\/1062936x.2023.2266364","journal-title":"SAR QSAR Environ Res"},{"key":"998_CR34","doi-asserted-by":"publisher","DOI":"10.1080\/07391102.2023.2218938","author":"T Ali","year":"2024","unstructured":"Ali T, Anjum F, Choudhury A et al (2024) Identification of natural product-based effective inhibitors of spleen tyrosine kinase (SYK) through virtual screening and molecular dynamics simulation approaches. J Biomol Struct Dyn. https:\/\/doi.org\/10.1080\/07391102.2023.2218938","journal-title":"J Biomol Struct Dyn"},{"key":"998_CR35","doi-asserted-by":"publisher","first-page":"3114","DOI":"10.3390\/molecules23123114","volume":"23","author":"X Wang","year":"2018","unstructured":"Wang X, Guo J, Ning Z et al (2018) Discovery of a natural Syk inhibitor from Chinese medicine through a docking-based virtual screening and biological assay study. Molecules 23:3114. https:\/\/doi.org\/10.3390\/molecules23123114","journal-title":"Molecules"},{"key":"998_CR36","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s12860-018-0174-z","volume":"19","author":"S Jieensinue","year":"2018","unstructured":"Jieensinue S, Zhu H, Li G et al (2018) Tanshinone IIA reduces SW837 colorectal cancer cell viability via the promotion of mitochondrial fission by activating JNK-Mff signaling pathways. BMC Cell Biol 19:21. https:\/\/doi.org\/10.1186\/s12860-018-0174-z","journal-title":"BMC Cell Biol"},{"key":"998_CR37","doi-asserted-by":"publisher","first-page":"2453","DOI":"10.1039\/d0ra08769f","volume":"11","author":"MC Tung","year":"2021","unstructured":"Tung MC, Tsai KC, Fung KM et al (2021) Characterizing the structure-activity relationships of natural products, tanshinones, reveals their mode of action in inhibiting spleen tyrosine kinase. RSC Adv 11:2453\u20132461. https:\/\/doi.org\/10.1039\/d0ra08769f","journal-title":"RSC Adv"},{"key":"998_CR38","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1016\/j.bmcl.2009.02.049","volume":"19","author":"HZ Xie","year":"2009","unstructured":"Xie HZ, Li LL, Ren JX et al (2009) Pharmacophore modeling study based on known spleen tyrosine kinase inhibitors together with virtual screening for identifying novel inhibitors. Bioorg Med Chem Lett 19:1944\u20131949. https:\/\/doi.org\/10.1016\/j.bmcl.2009.02.049","journal-title":"Bioorg Med Chem Lett"},{"key":"998_CR39","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.compbiomed.2013.01.015","volume":"43","author":"BK Li","year":"2013","unstructured":"Li BK, Cong Y, Yang XG et al (2013) In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method. Comput Biol Med 43:395\u2013404. https:\/\/doi.org\/10.1016\/j.compbiomed.2013.01.015","journal-title":"Comput Biol Med"},{"key":"998_CR40","doi-asserted-by":"publisher","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","volume":"52","author":"B Zdrazil","year":"2024","unstructured":"Zdrazil B, Felix E, Hunter F et al (2024) The ChEMBL database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res 52:D1180\u2013D1192. https:\/\/doi.org\/10.1093\/nar\/gkad1004","journal-title":"Nucleic Acids Res"},{"key":"998_CR41","doi-asserted-by":"publisher","first-page":"D1045","DOI":"10.1093\/nar\/gkv1072","volume":"44","author":"MK Gilson","year":"2016","unstructured":"Gilson MK, Liu T, Baitaluk M et al (2016) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44:D1045\u2013D1053. https:\/\/doi.org\/10.1093\/nar\/gkv1072","journal-title":"Nucleic Acids Res"},{"key":"998_CR42","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-3-031-20611-5_3","volume":"1685","author":"MSR Campos","year":"2022","unstructured":"Campos MSR, L\u00f3pez DAG, Rivera JAC et al (2022) Bioactivity predictors for the inhibition of Staphylococcus aureus quinolone resistance protein. Commun Comput Inf Sci 1685:31\u201340. https:\/\/doi.org\/10.1007\/978-3-031-20611-5_3","journal-title":"Commun Comput Inf Sci"},{"key":"998_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmcr.2024.100148","volume":"11","author":"K Singh","year":"2024","unstructured":"Singh K, Ghosh I, Jayaprakash V et al (2024) Building a ML-based QSAR model for predicting the bioactivity of therapeutically active drug class with imidazole scaffold. Eur J Med Chem Rep 11:100148. https:\/\/doi.org\/10.1016\/j.ejmcr.2024.100148","journal-title":"Eur J Med Chem Rep"},{"key":"998_CR44","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701","volume-title":"Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining","author":"T Akiba","year":"2019","unstructured":"Akiba T, Sano S, Yanase T et al (2019) Optuna: a next-generation hyperparameter optimization framework. In: Akiba T (ed) Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. Association for computing machinery, New York. https:\/\/doi.org\/10.1145\/3292500.3330701"},{"key":"998_CR45","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1039\/b918972f","volume":"135","author":"RG Brereton","year":"2010","unstructured":"Brereton RG, Lloyd GR (2010) Support vector machines for classification and regression. Analyst 135:230\u2013267. https:\/\/doi.org\/10.1039\/b918972f","journal-title":"Analyst"},{"key":"998_CR46","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1186\/s13321-020-00473-0","volume":"12","author":"T Blaschke","year":"2020","unstructured":"Blaschke T, Engkvist O, Bajorath J et al (2020) Memory-assisted reinforcement learning for diverse molecular de novo design. J Cheminform 12:68. https:\/\/doi.org\/10.1186\/s13321-020-00473-0","journal-title":"J Cheminform"},{"key":"998_CR47","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1038\/s42004-022-00733-0","volume":"5","author":"M Korshunova","year":"2022","unstructured":"Korshunova M, Huang N, Capuzzi S et al (2022) Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds. Commun Chem 5:129. https:\/\/doi.org\/10.1038\/s42004-022-00733-0","journal-title":"Commun Chem"},{"key":"998_CR48","unstructured":"RCSB PDB-3FQS: crystal structure of spleen tyrosine kinase complexed with R406.\u00a0https:\/\/www.rcsb.org\/structure\/3FQS. Accessed 1 Mar 2025."},{"key":"998_CR49","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2401.09840","author":"A Telepov","year":"2024","unstructured":"Telepov A, Tsypin A, Khrabrov K et al (2024) FREED++: improving RL agents for fragment-based molecule generation by thorough reproduction. arXiv preprint. https:\/\/doi.org\/10.48550\/arXiv.2401.09840","journal-title":"arXiv preprint"},{"key":"998_CR50","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/1758-2946-1-8","volume":"1","author":"P Ertl","year":"2009","unstructured":"Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform 1:8. https:\/\/doi.org\/10.1186\/1758-2946-1-8","journal-title":"J Cheminform"},{"key":"998_CR51","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nchem.1243","volume":"4","author":"GR Bickerton","year":"2012","unstructured":"Bickerton GR, Paolini GV, Besnard J et al (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90\u201398. https:\/\/doi.org\/10.1038\/nchem.1243","journal-title":"Nat Chem"},{"key":"998_CR52","doi-asserted-by":"publisher","first-page":"W422","DOI":"10.1093\/nar\/gkae236","volume":"52","author":"L Fu","year":"2024","unstructured":"Fu L, Shi S, Yi J et al (2024) ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support. Nucleic Acids Res 52:W422\u2013W431. https:\/\/doi.org\/10.1093\/nar\/gkae236","journal-title":"Nucleic Acids Res"},{"key":"998_CR53","doi-asserted-by":"publisher","DOI":"10.1002\/prp2.175","volume":"3","author":"MG Rolf","year":"2015","unstructured":"Rolf MG, Curwen JO, Veldman-Jones M et al (2015) In vitro pharmacological profiling of R406 identifies molecular targets underlying the clinical effects of fostamatinib. Pharmacol Res Perspect 3:e00175. https:\/\/doi.org\/10.1002\/prp2.175","journal-title":"Pharmacol Res Perspect"},{"key":"998_CR54","doi-asserted-by":"publisher","DOI":"10.1111\/sji.13371","volume":"100","author":"X Hu","year":"2024","unstructured":"Hu X, Hu C, Liao L et al (2024) Isoliquiritigenin limits inflammasome activation of macrophage via docking into Syk to alleviate murine non-alcoholic fatty liver disease. Scand J Immunol 100:e13371. https:\/\/doi.org\/10.1111\/sji.13371","journal-title":"Scand J Immunol"},{"key":"998_CR55","doi-asserted-by":"publisher","first-page":"7009","DOI":"10.3390\/ijms21197009","volume":"21","author":"G Marchetti","year":"2020","unstructured":"Marchetti G, Dess\u00ec A, Dallocchio R et al (2020) Syk inhibitors: new computational insights into their intraerythrocytic action in Plasmodium falciparum malaria. Int J Mol Sci 21:7009. https:\/\/doi.org\/10.3390\/ijms21197009","journal-title":"Int J Mol Sci"},{"key":"998_CR56","doi-asserted-by":"publisher","first-page":"394632024128203","DOI":"10.1177\/03946320241282030","volume":"38","author":"M Mansouri","year":"2024","unstructured":"Mansouri M, ElHaddoumi G, Kandoussi I et al (2024) Syk protein inhibitors treatment for the allergic symptoms associated with hyper immunoglobulin E syndromes: a focused on a computational approach. Int J Immunopathol Pharmacol 38:3946320241282030. https:\/\/doi.org\/10.1177\/03946320241282030","journal-title":"Int J Immunopathol Pharmacol"},{"key":"998_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.bioorg.2024.107320","volume":"146","author":"L Wang","year":"2024","unstructured":"Wang L, Fang Y, Ma Y et al (2024) A novel natural Syk inhibitor suppresses IgE-mediated mast cell activation and passive cutaneous anaphylaxis. Bioorg Chem 146:107320. https:\/\/doi.org\/10.1016\/j.bioorg.2024.107320","journal-title":"Bioorg Chem"},{"key":"998_CR58","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742\u2013754. https:\/\/doi.org\/10.1021\/ci100050t","journal-title":"J Chem Inf Model"},{"key":"998_CR59","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1021\/acs.jcim.7b00616","volume":"58","author":"S Jaeger","year":"2018","unstructured":"Jaeger S, Fulle S, Turk S (2018) Mol2vec: unsupervised machine learning approach with chemical intuition. J Chem Inf Model 58:27\u201335. https:\/\/doi.org\/10.1021\/acs.jcim.7b00616","journal-title":"J Chem Inf Model"},{"key":"998_CR60","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1021\/ci010132r","volume":"42","author":"JL Durant","year":"2002","unstructured":"Durant JL, Leland BA, Henry DR et al (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42:1273\u20131280. https:\/\/doi.org\/10.1021\/ci010132r","journal-title":"J Chem Inf Comput Sci"},{"key":"998_CR61","doi-asserted-by":"publisher","first-page":"D1516","DOI":"10.1093\/nar\/gkae1059","volume":"53","author":"S Kim","year":"2025","unstructured":"Kim S, Chen J, Cheng T et al (2025) PubChem 2025 update. Nucleic Acids Res 53:D1516\u2013D1525. https:\/\/doi.org\/10.1093\/nar\/gkae1059","journal-title":"Nucleic Acids Res"},{"key":"998_CR62","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s13321-020-00445-4","volume":"12","author":"A Capecchi","year":"2020","unstructured":"Capecchi A, Probst D, Reymond JL (2020) One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. J Cheminform 12:43. https:\/\/doi.org\/10.1186\/s13321-020-00445-4","journal-title":"J Cheminform"},{"key":"998_CR63","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1021\/tx200211v","volume":"24","author":"NA Meanwell","year":"2011","unstructured":"Meanwell NA (2011) Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety. Chem Res Toxicol 24:1420\u20131456. https:\/\/doi.org\/10.1021\/tx200211v","journal-title":"Chem Res Toxicol"},{"key":"998_CR64","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1021\/mp100444g","volume":"8","author":"S Tian","year":"2011","unstructured":"Tian S, Li Y, Wang J et al (2011) ADME evaluation in drug discovery. 9. Prediction of oral bioavailability in humans based on molecular properties and structural fingerprints. Mol Pharm 8:841\u2013851. https:\/\/doi.org\/10.1021\/mp100444g","journal-title":"Mol Pharm"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-00998-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-00998-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-00998-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T09:52:12Z","timestamp":1744710732000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-00998-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,15]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["998"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-00998-2","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,15]]},"assertion":[{"value":"17 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"52"}}