{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T01:49:48Z","timestamp":1780710588552,"version":"3.54.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T00:00:00Z","timestamp":1705968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["MOST-110-2221-E-400-004-MY3"],"award-info":[{"award-number":["MOST-110-2221-E-400-004-MY3"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004737","name":"National Health Research Institutes","doi-asserted-by":"publisher","award":["BP-111-SP-01"],"award-info":[{"award-number":["BP-111-SP-01"]}],"id":[{"id":"10.13039\/501100004737","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The drug discovery of G protein-coupled receptors (GPCRs) superfamily using computational models is often limited by the availability of protein three-dimensional (3D) structures and chemicals with experimentally measured bioactivities. Orphan GPCRs without known ligands further complicate the process. To enable drug discovery for human orphan GPCRs, multitask models were proposed for predicting half maximal effective concentrations (EC<jats:sub>50<\/jats:sub>) of the pairs of chemicals and GPCRs. Protein multiple sequence alignment features, and physicochemical properties and fingerprints of chemicals were utilized to encode the protein and chemical information, respectively. The protein features enabled the transfer of data-rich GPCRs to orphan receptors and the transferability based on the similarity of protein features. The final model was trained using both agonist and antagonist data from 200 GPCRs and showed an excellent mean squared error (MSE) of 0.24 in the validation dataset. An independent test using the orphan dataset consisting of 16 receptors associated with less than 8 bioactivities showed a reasonably good MSE of 1.51 that can be further improved to 0.53 by considering the transferability based on protein features. The informative features were identified and mapped to corresponding 3D structures to gain insights into the mechanism of GPCR-ligand interactions across the GPCR family. The proposed method provides a novel perspective on learning ligand bioactivity within the diverse human GPCR superfamily and can potentially accelerate the discovery of therapeutic agents for orphan GPCRs.<\/jats:p>","DOI":"10.1186\/s13321-024-00806-3","type":"journal-article","created":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T07:02:36Z","timestamp":1705993356000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Decrypting orphan GPCR drug discovery via multitask learning"],"prefix":"10.1186","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4437-3530","authenticated-orcid":false,"given":"Wei-Cheng","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5200-7605","authenticated-orcid":false,"given":"Wei-Ting","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1742-7655","authenticated-orcid":false,"given":"Ming-Shiu","family":"Hung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinq-Chyi","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3011-8440","authenticated-orcid":false,"given":"Chun-Wei","family":"Tung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"key":"806_CR1","doi-asserted-by":"publisher","first-page":"4461","DOI":"10.1007\/s00018-019-03276-1","volume":"76","author":"AV Fonin","year":"2019","unstructured":"Fonin AV, Darling AL, Kuznetsova IM, Turoverov KK, Uversky VN (2019) Multi-functionality of proteins involved in GPCR and G protein signaling: making sense of structure-function continuum with intrinsic disorder-based proteoforms. Cell Mol Life Sci 76:4461\u20134492. https:\/\/doi.org\/10.1007\/s00018-019-03276-1","journal-title":"Cell Mol Life Sci"},{"key":"806_CR2","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/s0165-6147(00)01678-3","volume":"22","author":"MJ Marinissen","year":"2001","unstructured":"Marinissen MJ, Gutkind JS (2001) G-protein-coupled receptors and signaling networks: emerging paradigms. Trends Pharmacol Sci 22:368\u2013376. https:\/\/doi.org\/10.1016\/s0165-6147(00)01678-3","journal-title":"Trends Pharmacol Sci"},{"key":"806_CR3","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.ygeno.2006.04.001","volume":"88","author":"TK Bjarnadottir","year":"2006","unstructured":"Bjarnadottir TK, Gloriam DE, Hellstrand SH, Kristiansson H, Fredriksson R, Schioth HB (2006) Comprehensive repertoire and phylogenetic analysis of the G protein-coupled receptors in human and mouse. Genomics 88:263\u2013273. https:\/\/doi.org\/10.1016\/j.ygeno.2006.04.001","journal-title":"Genomics"},{"key":"806_CR4","doi-asserted-by":"publisher","first-page":"2471","DOI":"10.1093\/molbev\/msr061","volume":"28","author":"KJ Nordstrom","year":"2011","unstructured":"Nordstrom KJ, Sallman Almen M, Edstam MM, Fredriksson R, Schioth HB (2011) Independent HHsearch, Needleman\u2013Wunsch-based, and motif analyses reveal the overall hierarchy for most of the G protein-coupled receptor families. Mol Biol Evol 28:2471\u20132480. https:\/\/doi.org\/10.1093\/molbev\/msr061","journal-title":"Mol Biol Evol"},{"key":"806_CR5","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1186\/s12967-021-03067-y","volume":"19","author":"K Tuzim","year":"2021","unstructured":"Tuzim K, Korolczuk A (2021) An update on extra-oral bitter taste receptors. J Transl Med 19:440. https:\/\/doi.org\/10.1186\/s12967-021-03067-y","journal-title":"J Transl Med"},{"key":"806_CR6","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1038\/nrm908","volume":"3","author":"KL Pierce","year":"2002","unstructured":"Pierce KL, Premont RT, Lefkowitz RJ (2002) Seven-transmembrane receptors. Nat Rev Mol Cell Biol 3:639\u2013650. https:\/\/doi.org\/10.1038\/nrm908","journal-title":"Nat Rev Mol Cell Biol"},{"key":"806_CR7","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1124\/mol.117.111062","volume":"93","author":"K Sriram","year":"2018","unstructured":"Sriram K, Insel PA (2018) G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol Pharmacol 93:251\u2013258. https:\/\/doi.org\/10.1124\/mol.117.111062","journal-title":"Mol Pharmacol"},{"key":"806_CR8","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/978-1-4939-7465-8_21","volume":"1705","author":"C Diaz","year":"2018","unstructured":"Diaz C, Angelloz-Nicoud P, Pihan E (2018) Modeling and Deorphanization of Orphan GPCRs. Methods Mol Biol 1705:413\u2013429. https:\/\/doi.org\/10.1007\/978-1-4939-7465-8_21","journal-title":"Methods Mol Biol"},{"key":"806_CR9","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583\u2013589. https:\/\/doi.org\/10.1038\/s41586-021-03819-2","journal-title":"Nature"},{"key":"806_CR10","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac308","author":"C Lee","year":"2022","unstructured":"Lee C, Su BH, Tseng YJ (2022) Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors. Brief Bioinform. https:\/\/doi.org\/10.1093\/bib\/bbac308","journal-title":"Brief Bioinform"},{"key":"806_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.sbi.2022.102495","volume":"77","author":"Z Peng","year":"2022","unstructured":"Peng Z, Wang W, Han R, Zhang F, Yang J (2022) Protein structure prediction in the deep learning era. Curr Opin Struct Biol 77:102495. https:\/\/doi.org\/10.1016\/j.sbi.2022.102495","journal-title":"Curr Opin Struct Biol"},{"key":"806_CR12","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1186\/s12859-022-04877-7","volume":"23","author":"J Oh","year":"2022","unstructured":"Oh J, Ceong HT, Na D, Park C (2022) A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists. BMC Bioinformatics 23:346. https:\/\/doi.org\/10.1186\/s12859-022-04877-7","journal-title":"BMC Bioinformatics"},{"key":"806_CR13","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1016\/j.cell.2017.07.009","volume":"170","author":"D Wacker","year":"2017","unstructured":"Wacker D, Stevens RC, Roth BL (2017) How ligands illuminate GPCR molecular pharmacology. Cell 170:414\u2013427. https:\/\/doi.org\/10.1016\/j.cell.2017.07.009","journal-title":"Cell"},{"key":"806_CR14","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1186\/s13321-021-00516-0","volume":"13","author":"M Thomas","year":"2021","unstructured":"Thomas M, Smith RT, O\u2019boyle NM, De Graaf C, Bender A, (2021) Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study. J Cheminform 13:39. https:\/\/doi.org\/10.1186\/s13321-021-00516-0","journal-title":"J Cheminform"},{"key":"806_CR15","doi-asserted-by":"publisher","first-page":"2564","DOI":"10.1016\/j.csbj.2022.05.016","volume":"20","author":"P Yadav","year":"2022","unstructured":"Yadav P, Mollaei P, Cao Z, Wang Y, Barati Farimani A (2022) Prediction of GPCR activity using machine learning. Comput Struct Biotechnol J 20:2564\u20132573. https:\/\/doi.org\/10.1016\/j.csbj.2022.05.016","journal-title":"Comput Struct Biotechnol J"},{"key":"806_CR16","doi-asserted-by":"publisher","DOI":"10.3390\/ijms23073780","author":"X Wang","year":"2022","unstructured":"Wang X, Liu J, Zhang C, Wang S (2022) SSGraphCPI: A novel model for predicting compound-protein interactions based on deep learning. Int J Mol Sci. https:\/\/doi.org\/10.3390\/ijms23073780","journal-title":"Int J Mol Sci"},{"key":"806_CR17","doi-asserted-by":"publisher","DOI":"10.1093\/bioadv\/vbab031","author":"JPL Velloso","year":"2021","unstructured":"Velloso JPL, Ascher DB, Pires DEV (2021) pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures. Bioinform Adv. https:\/\/doi.org\/10.1093\/bioadv\/vbab031","journal-title":"Bioinform Adv"},{"key":"806_CR18","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.1002\/prot.25071","volume":"84","author":"W Nemoto","year":"2016","unstructured":"Nemoto W, Yamanishi Y, Limviphuvadh V, Saito A, Toh H (2016) GGIP: structure and sequence-based GPCR-GPCR interaction pair predictor. Proteins 84:1224\u20131233. https:\/\/doi.org\/10.1002\/prot.25071","journal-title":"Proteins"},{"key":"806_CR19","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.825195","volume":"13","author":"W Nemoto","year":"2022","unstructured":"Nemoto W, Yamanishi Y, Limviphuvadh V, Fujishiro S, Shimamura S, Fukushima A et al (2022) A web server for GPCR-GPCR interaction pair prediction. Front Endocrinol (Lausanne) 13:825195. https:\/\/doi.org\/10.3389\/fendo.2022.825195","journal-title":"Front Endocrinol (Lausanne)"},{"key":"806_CR20","doi-asserted-by":"publisher","first-page":"2934","DOI":"10.1111\/bph.13452","volume":"173","author":"T Ngo","year":"2016","unstructured":"Ngo T, Kufareva I, Coleman J, Graham RM, Abagyan R, Smith NJ (2016) Identifying ligands at orphan GPCRs: current status using structure-based approaches. Br J Pharmacol 173:2934\u20132951. https:\/\/doi.org\/10.1111\/bph.13452","journal-title":"Br J Pharmacol"},{"key":"806_CR21","doi-asserted-by":"publisher","first-page":"863","DOI":"10.3390\/biom12070863","volume":"12","author":"KO Dankwah","year":"2022","unstructured":"Dankwah KO, Mohl JE, Begum K, Leung MY (2022) What Makes GPCRs from Different Families Bind to the Same Ligand? Biomolecules 12:863. https:\/\/doi.org\/10.3390\/biom12070863","journal-title":"Biomolecules"},{"key":"806_CR22","doi-asserted-by":"publisher","first-page":"D335","DOI":"10.1093\/nar\/gkaa1080","volume":"49","author":"AJ Kooistra","year":"2021","unstructured":"Kooistra AJ, Mordalski S, Pandy-Szekeres G, Esguerra M, Mamyrbekov A, Munk C et al (2021) GPCRdb in 2021: integrating GPCR sequence, structure and function. Nucleic Acids Res 49:D335\u2013D343. https:\/\/doi.org\/10.1093\/nar\/gkaa1080","journal-title":"Nucleic Acids Res"},{"key":"806_CR23","doi-asserted-by":"publisher","first-page":"D930","DOI":"10.1093\/nar\/gky1075","volume":"47","author":"D Mendez","year":"2019","unstructured":"Mendez D, Gaulton A, Bento AP, Chambers J, De Veij M, Felix E et al (2019) ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res 47:D930\u2013D940. https:\/\/doi.org\/10.1093\/nar\/gky1075","journal-title":"Nucleic Acids Res"},{"key":"806_CR24","doi-asserted-by":"publisher","first-page":"D480","DOI":"10.1093\/nar\/gkaa1100","volume":"49","author":"C Uniprot","year":"2021","unstructured":"Uniprot C (2021) UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 49:D480\u2013D489. https:\/\/doi.org\/10.1093\/nar\/gkaa1100","journal-title":"Nucleic Acids Res"},{"key":"806_CR25","doi-asserted-by":"publisher","first-page":"1792","DOI":"10.1093\/nar\/gkh340","volume":"32","author":"RC Edgar","year":"2004","unstructured":"Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792\u20131797. https:\/\/doi.org\/10.1093\/nar\/gkh340","journal-title":"Nucleic Acids Res"},{"key":"806_CR26","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/s13321-016-0160-4","volume":"8","author":"RM Hanson","year":"2016","unstructured":"Hanson RM (2016) Jmol SMILES and Jmol SMARTS: specifications and applications. J Cheminform 8:50. https:\/\/doi.org\/10.1186\/s13321-016-0160-4","journal-title":"J Cheminform"},{"key":"806_CR27","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1002\/jcc.21707","volume":"32","author":"CW Yap","year":"2011","unstructured":"Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466\u20131474. https:\/\/doi.org\/10.1002\/jcc.21707","journal-title":"J Comput Chem"},{"key":"806_CR28","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s13321-020-00456-1","volume":"12","author":"AP Bento","year":"2020","unstructured":"Bento AP, Hersey A, Felix E, Landrum G, Gaulton A, Atkinson F et al (2020) An open source chemical structure curation pipeline using RDKit. J Cheminform 12:51. https:\/\/doi.org\/10.1186\/s13321-020-00456-1","journal-title":"J Cheminform"},{"key":"806_CR29","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":"806_CR30","unstructured":"Ke G, Meng Q, Finely T, Wang T, Chen W, Ma W, et al. (2017) LightGBM: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30 (NIP 2017)"},{"key":"806_CR31","doi-asserted-by":"publisher","unstructured":"Dorogush AV, Ershov V, Gulin A (2018) CatBoost: gradient boosting with categorical features support. arXiv: 1810.11363. https:\/\/doi.org\/10.48550\/arXiv.1810.11363","DOI":"10.48550\/arXiv.1810.11363"},{"key":"806_CR32","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach Learn"},{"key":"806_CR33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 63:3\u201342. https:\/\/doi.org\/10.1007\/s10994-006-6226-1","journal-title":"Mach Learn"},{"key":"806_CR34","unstructured":"Ting K, Witten I (1997) Stacking bagged and dagged models. Int Conf Mach Learn"},{"key":"806_CR35","doi-asserted-by":"publisher","unstructured":"Erickson N, Mueller J, Shirkov A, Zhang H, Larroy P, Li M, et al. (2020) AutoGluon-Tabular: robust and accurate autoML for structured data. arXiv: 2003.06505. https:\/\/doi.org\/10.48550\/arXiv.2003.06505","DOI":"10.48550\/arXiv.2003.06505"},{"key":"806_CR36","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s12859-016-1423-9","volume":"18","author":"M Radovic","year":"2017","unstructured":"Radovic M, Ghalwash M, Filipovic N, Obradovic Z (2017) Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinformatics 18:9. https:\/\/doi.org\/10.1186\/s12859-016-1423-9","journal-title":"BMC Bioinformatics"},{"key":"806_CR37","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":"806_CR38","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1038\/nature14886","volume":"524","author":"W Huang","year":"2015","unstructured":"Huang W, Manglik A, Venkatakrishnan AJ, Laeremans T, Feinberg EN, Sanborn AL et al (2015) Structural insights into \u03bc-opioid receptor activation. Nature 524:315\u2013321. https:\/\/doi.org\/10.1038\/nature14886","journal-title":"Nature"},{"key":"806_CR39","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1172\/JCI21927","volume":"114","author":"S Srinivasan","year":"2004","unstructured":"Srinivasan S, Lubrano-Berthelier C, Govaerts C, Picard F, Santiago P, Conklin BR et al (2004) Constitutive activity of the melanocortin-4 receptor is maintained by its N-terminal domain and plays a role in energy homeostasis in humans. J Clin Invest 114:1158\u20131164. https:\/\/doi.org\/10.1172\/JCI21927","journal-title":"J Clin Invest"},{"key":"806_CR40","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1016\/j.cell.2016.10.004","volume":"167","author":"T Hua","year":"2016","unstructured":"Hua T, Vemuri K, Pu M, Qu L, Han GW, Wu Y et al (2016) Crystal structure of the human cannabinoid receptor CB1. Cell 167:750-762.e714. https:\/\/doi.org\/10.1016\/j.cell.2016.10.004","journal-title":"Cell"},{"key":"806_CR41","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1038\/nature10954","volume":"485","author":"A Manglik","year":"2012","unstructured":"Manglik A, Kruse AC, Kobilka TS, Thian FS, Mathiesen JM, Sunahara RK et al (2012) Crystal structure of the \u03bc-opioid receptor bound to a morphinan antagonist. Nature 485:321\u2013326. https:\/\/doi.org\/10.1038\/nature10954","journal-title":"Nature"},{"key":"806_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cellsig.2017.02.004","volume":"33","author":"JL Coleman","year":"2017","unstructured":"Coleman JL, Ngo T, Smith NJ (2017) The G protein-coupled receptor N-terminus and receptor signalling: N-tering a new era. Cell Signal 33:1\u20139. https:\/\/doi.org\/10.1016\/j.cellsig.2017.02.004","journal-title":"Cell Signal"},{"key":"806_CR43","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1038\/s41589-023-01328-z","volume":"19","author":"X Zhao","year":"2023","unstructured":"Zhao X, Stein KR, Chen V, Griffin ME, Lairson LL, Hang HC (2023) Chemoproteomics reveals microbiota-derived aromatic monoamine agonists for GPRC5A. Nat Chem Biol 19:1205\u20131214. https:\/\/doi.org\/10.1038\/s41589-023-01328-z","journal-title":"Nat Chem Biol"},{"issue":"4361\u20134375","key":"806_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2022.09.041","volume":"185","author":"Y Zhuang","year":"2022","unstructured":"Zhuang Y, Wang Y, He B, He X, Zhou XE, Guo S et al (2022) Molecular recognition of morphine and fentanyl by the human mu-opioid receptor. Cell 185(4361\u20134375):e4319. https:\/\/doi.org\/10.1016\/j.cell.2022.09.041","journal-title":"Cell"},{"key":"806_CR45","doi-asserted-by":"publisher","first-page":"4137","DOI":"10.1038\/s41467-020-17791-4","volume":"11","author":"M Dong","year":"2020","unstructured":"Dong M, Deganutti G, Piper SJ, Liang YL, Khoshouei M, Belousoff MJ et al (2020) Structure and dynamics of the active Gs-coupled human secretin receptor. Nat Commun 11:4137. https:\/\/doi.org\/10.1038\/s41467-020-17791-4","journal-title":"Nat Commun"},{"key":"806_CR46","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1007\/s13238-020-00808-5","volume":"12","author":"J Zhang","year":"2021","unstructured":"Zhang J, Qu L, Wu L, Tang X, Luo F, Xu W et al (2021) Structural insights into the activation initiation of full-length mGlu1. Protein Cell 12:662\u2013667. https:\/\/doi.org\/10.1007\/s13238-020-00808-5","journal-title":"Protein Cell"},{"key":"806_CR47","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abm5563","author":"M Kinnebrew","year":"2022","unstructured":"Kinnebrew M, Woolley RE, Ansell TB, Byrne EFX, Frigui S, Luchetti G et al (2022) Patched 1 regulates Smoothened by controlling sterol binding to its extracellular cysteine-rich domain. Sci Adv. https:\/\/doi.org\/10.1126\/sciadv.abm5563","journal-title":"Sci Adv"},{"key":"806_CR48","doi-asserted-by":"publisher","first-page":"16342","DOI":"10.1073\/pnas.1205838109","volume":"109","author":"D El Moustaine","year":"2012","unstructured":"El Moustaine D, Granier S, Doumazane E, Scholler P, Rahmeh R, Bron P et al (2012) Distinct roles of metabotropic glutamate receptor dimerization in agonist activation and G-protein coupling. Proc Natl Acad Sci U S A 109:16342\u201316347. https:\/\/doi.org\/10.1073\/pnas.1205838109","journal-title":"Proc Natl Acad Sci U S A"},{"key":"806_CR49","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1126\/science.1249489","volume":"344","author":"H Wu","year":"2014","unstructured":"Wu H, Wang C, Gregory KJ, Han GW, Cho HP, Xia Y et al (2014) Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator. Science 344:58\u201364. https:\/\/doi.org\/10.1126\/science.1249489","journal-title":"Science"},{"key":"806_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2023.107911","volume":"106","author":"S Haroon","year":"2023","unstructured":"Haroon S, C AH, A SJ, (2023) Generative pre-trained transformer (GPT) based model with relative attention for de novo drug design. Comput Biol Chem 106:107911. https:\/\/doi.org\/10.1016\/j.compbiolchem.2023.107911","journal-title":"Comput Biol Chem"},{"key":"806_CR51","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0141287","volume":"10","author":"E Asgari","year":"2015","unstructured":"Asgari E, Mofrad MR (2015) Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS ONE 10:e0141287. https:\/\/doi.org\/10.1371\/journal.pone.0141287","journal-title":"PLoS ONE"},{"key":"806_CR52","unstructured":"Nvidia (2022) MegaMolBART: generally applicable chemical AI models with large-scale pretrained transformers. https:\/\/github.com\/NVIDIA\/MegaMolBART"},{"key":"806_CR53","first-page":"988","volume":"9","author":"Y Zhou","year":"2010","unstructured":"Zhou Y, Jin R, Hoi SCH (2010) Exclusive Lasso for multi-task feature selection. Proc Thirteenth Int Conf Artif Intell Stat PMLR 9:988\u2013995","journal-title":"Proc Thirteenth Int Conf Artif Intell Stat PMLR"},{"key":"806_CR54","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2023.3294413","author":"C Yu","year":"2023","unstructured":"Yu C, Cui D, Shang M, Zhang S, Guo L, Han J et al (2023) A multi-task deep feature selection method for brain imaging genetics. IEEE\/ACM Trans Comput Biol Bioinform. https:\/\/doi.org\/10.1109\/TCBB.2023.3294413","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-024-00806-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-024-00806-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-024-00806-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,23]],"date-time":"2024-01-23T07:11:57Z","timestamp":1705993917000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-024-00806-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,23]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["806"],"URL":"https:\/\/doi.org\/10.1186\/s13321-024-00806-3","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,23]]},"assertion":[{"value":"28 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2024","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 conflict of financial interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"10"}}