{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T21:32:30Z","timestamp":1779226350724,"version":"3.51.4"},"reference-count":68,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T00:00:00Z","timestamp":1695340800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023B1515020042"],"award-info":[{"award-number":["2023B1515020042"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81973241"],"award-info":[{"award-number":["81973241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Artificial intelligence-based molecular property prediction plays a key role in molecular design such as bioactive molecules and functional materials. In this study, we propose a self-supervised pretraining deep learning (DL) framework, called functional group bidirectional encoder representations from transformers (FG-BERT), pertained based on ~1.45 million unlabeled drug-like molecules, to learn meaningful representation of molecules from function groups. The pretrained FG-BERT framework can be fine-tuned to predict molecular properties. Compared to state-of-the-art (SOTA) machine learning and DL methods, we demonstrate the high performance of FG-BERT in evaluating molecular properties in tasks involving physical chemistry, biophysics and physiology across 44 benchmark datasets. In addition, FG-BERT utilizes attention mechanisms to focus on FG features that are critical to the target properties, thereby providing excellent interpretability for downstream training tasks. Collectively, FG-BERT does not require any artificially crafted features as input and has excellent interpretability, providing an out-of-the-box framework for developing SOTA models for a variety of molecule (especially for drug) discovery tasks.<\/jats:p>","DOI":"10.1093\/bib\/bbad398","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T01:10:40Z","timestamp":1699405840000},"source":"Crossref","is-referenced-by-count":39,"title":["FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for properties prediction"],"prefix":"10.1093","volume":"24","author":[{"given":"Biaoshun","family":"Li","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering , Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, , Guangzhou 510006 , China"},{"name":"South China University of Technology , Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, , Guangzhou 510006 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mujie","family":"Lin","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering , Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, , Guangzhou 510006 , China"},{"name":"South China University of Technology , Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, , Guangzhou 510006 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiegen","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Room 109, Building C, SSIP Healthcare and Medicine Demonstration Zone , Zhongshan Tsuihang New District, Zhongshan, Guangdong, 528400 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5116-7749","authenticated-orcid":false,"given":"Ling","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering , Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, , Guangzhou 510006 , China"},{"name":"South China University of Technology , Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, , Guangzhou 510006 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,11,2]]},"reference":[{"key":"2023110801102730700_ref1","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1093\/bib\/bbp023","article-title":"Recent advances in computer-aided drug design","volume":"10","author":"Song","year":"2009","journal-title":"Brief Bioinform"},{"key":"2023110801102730700_ref2","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1021\/ci400573c","article-title":"Choosing feature selection and learning algorithms in QSAR","volume":"54","author":"Eklund","year":"2014","journal-title":"J Chem Inf Model"},{"key":"2023110801102730700_ref3","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0278-6915(90)90112-Z","article-title":"Survey of the QSAR and in vitro approaches for developing non-animal methods to supersede the in vivo LD50 test","volume":"28","author":"Phillips","year":"1990","journal-title":"Food Chem Toxicol"},{"key":"2023110801102730700_ref4","first-page":"2702","article-title":"Discriminative embeddings of latent variable models for structured data","author":"Dai","year":"2016","journal-title":"Int Conf Mach Learn"},{"key":"2023110801102730700_ref5","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1038\/s42256-022-00501-8","article-title":"An adaptive graph learning method for automated molecular interactions and properties predictions","volume":"4","author":"Li","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2023110801102730700_ref6","doi-asserted-by":"crossref","first-page":"bbac131","DOI":"10.1093\/bib\/bbac131","article-title":"Knowledge-based BERT: a method to extract molecular features like computational chemists","volume":"23","author":"Wu","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023110801102730700_ref7","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1145\/3307339.3342186","volume-title":"Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","author":"Wang","year":"2019"},{"key":"2023110801102730700_ref8","first-page":"1","article-title":"Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework","author":"Zeng","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2023110801102730700_ref9","doi-asserted-by":"crossref","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","article-title":"Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism","volume":"63","author":"Xiong","year":"2019","journal-title":"J Med Chem"},{"key":"2023110801102730700_ref10","doi-asserted-by":"crossref","first-page":"bbab112","DOI":"10.1093\/bib\/bbab112","article-title":"Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method","volume":"22","author":"Wu","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023110801102730700_ref11","doi-asserted-by":"crossref","first-page":"bbac408","DOI":"10.1093\/bib\/bbac408","article-title":"FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction","volume":"23","author":"Cai","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023110801102730700_ref12","doi-asserted-by":"crossref","first-page":"115401","DOI":"10.1016\/j.ejmech.2023.115401","article-title":"DeepCancerMap: a versatile deep learning platform for target-and cell-based anticancer drug discovery","volume":"255","author":"Wu","year":"2023","journal-title":"Eur J Med Chem"},{"key":"2023110801102730700_ref13","doi-asserted-by":"crossref","first-page":"971369","DOI":"10.3389\/fphar.2022.971369","article-title":"A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors","volume":"13","author":"Ai","year":"2022","journal-title":"Front Pharmacol"},{"key":"2023110801102730700_ref14","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1021\/acs.jcim.2c01099","article-title":"HiGNN: a hierarchical informative graph neural network for molecular property prediction equipped with feature-wise attention","volume":"63","author":"Zhu","year":"2023","journal-title":"J Chem Inf Model"},{"key":"2023110801102730700_ref15","first-page":"1","article-title":"Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models","volume":"13","author":"Jiang","year":"2021","journal-title":"J Chem"},{"key":"2023110801102730700_ref16","first-page":"857","article-title":"Self-supervised learning: generative or contrastive","volume":"35","author":"Liu","year":"2021","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2023110801102730700_ref17","article-title":"Attention is all you need[J]","volume-title":"Advances in Neural Information Processing Systems","year":"2017"},{"key":"2023110801102730700_ref18","doi-asserted-by":"crossref","first-page":"bbab152","DOI":"10.1093\/bib\/bbab152","article-title":"MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023110801102730700_ref19","first-page":"20479","article-title":"3d infomax improves gnns for molecular property prediction","author":"St\u00e4rk","year":"2022","journal-title":"Int Conf Mach Learn"},{"key":"2023110801102730700_ref20","article-title":"Pre-training molecular graph representation with 3d geometry","author":"Liu","year":"2021"},{"key":"2023110801102730700_ref21","article-title":"Mole-BERT: rethinking pre-training graph neural networks for molecules","author":"Xia","year":"2023","journal-title":"Elev Int Conf Learn"},{"key":"2023110801102730700_ref22","doi-asserted-by":"crossref","first-page":"8408","DOI":"10.1021\/acs.jmedchem.0c00754","article-title":"The most common functional groups in bioactive molecules and how their popularity has evolved over time","volume":"63","author":"Ertl","year":"2020","journal-title":"J Med Chem"},{"key":"2023110801102730700_ref23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10965-017-1369-2","article-title":"Influence of substitution of various functional groups on inhibition efficiency of TEMPO analogues on styrene polymerization","volume":"24","author":"Wadhwa","year":"2017","journal-title":"J Polym Res"},{"key":"2023110801102730700_ref24","doi-asserted-by":"crossref","first-page":"117755","DOI":"10.1016\/j.molliq.2021.117755","article-title":"Understanding functional group effect on corrosion inhibition efficiency of selected organic compounds","volume":"344","author":"Assad","year":"2021","journal-title":"J Mol Liq"},{"key":"2023110801102730700_ref25","first-page":"100022","article-title":"Learning functional group chemistry from molecular images leads to accurate prediction of activity cliffs","volume":"1","author":"Iqbal","year":"2021","journal-title":"Artif Intell Life Sci"},{"key":"2023110801102730700_ref26","doi-asserted-by":"crossref","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","article-title":"ChEMBL: a large-scale bioactivity database for drug discovery","volume":"40","author":"Gaulton","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023110801102730700_ref27","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1021\/ci034243x","article-title":"ESOL: estimating aqueous solubility directly from molecular structure","volume":"44","author":"Delaney","year":"2004","journal-title":"J Chem Inf Comput Sci"},{"key":"2023110801102730700_ref28","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/s10822-014-9747-x","article-title":"FreeSolv: a database of experimental and calculated hydration free energies, with input files","volume":"28","author":"Mobley","year":"2014","journal-title":"J Comput Aided Mol Des"},{"key":"2023110801102730700_ref29","doi-asserted-by":"crossref","first-page":"D930","DOI":"10.1093\/nar\/gky1075","article-title":"ChEMBL: towards direct deposition of bioassay data","volume":"47","author":"Mendez","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2023110801102730700_ref30","doi-asserted-by":"crossref","first-page":"2241","DOI":"10.1021\/jz200866s","article-title":"The Harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid","volume":"2","author":"Hachmann","year":"2011","journal-title":"J Phys Chem Lett"},{"key":"2023110801102730700_ref31","author":"AIDS antiviral screen data","year":"2017"},{"key":"2023110801102730700_ref32","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1038\/nature09107","article-title":"Thousands of chemical starting points for antimalarial lead identification","volume":"465","author":"Gamo","year":"2010","journal-title":"Nature"},{"key":"2023110801102730700_ref33","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1021\/ci8002649","article-title":"Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data","volume":"49","author":"Rohrer","year":"2009","journal-title":"J Chem Inf Model"},{"key":"2023110801102730700_ref34","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1021\/acs.jcim.6b00290","article-title":"Computational modeling of \u03b2-secretase 1 (BACE-1) inhibitors using ligand based approaches","volume":"56","author":"Subramanian","year":"2016","journal-title":"J Chem Inf Model"},{"key":"2023110801102730700_ref35","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1021\/ci300124c","article-title":"A Bayesian approach to in silico blood-brain barrier penetration modeling","volume":"52","author":"Martins","year":"2012","journal-title":"J Chem Inf Model"},{"key":"2023110801102730700_ref36","article-title":"Tox21 data challenge.","volume-title":"NIH","year":"2017"},{"key":"2023110801102730700_ref37","doi-asserted-by":"crossref","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","article-title":"The SIDER database of drugs and side effects","volume":"44","author":"Kuhn","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023110801102730700_ref38","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: a benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem Sci"},{"key":"2023110801102730700_ref39","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1016\/j.chembiol.2016.07.023","article-title":"A data-driven approach to predicting successes and failures of clinical trials","volume":"23","author":"Gayvert","year":"2016","journal-title":"Cell Chem Biol"},{"key":"2023110801102730700_ref40","doi-asserted-by":"crossref","first-page":"3766","DOI":"10.3389\/fphar.2021.796534","article-title":"Machine learning enables accurate and rapid prediction of active molecules against breast cancer cells","volume":"12","author":"He","year":"2021","journal-title":"Front Pharmacol"},{"key":"2023110801102730700_ref41","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018"},{"key":"2023110801102730700_ref42","article-title":"Adam: a method for stochastic optimization.","volume-title":"International Conference on Learning Representations","year":"2015"},{"key":"2023110801102730700_ref43","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"2023110801102730700_ref44","article-title":"Hyperopt: distributed asynchronous hyper-parameter optimization","author":"Bergstra","year":"2022","journal-title":"Astrophys Source Code Libr"},{"key":"2023110801102730700_ref45","doi-asserted-by":"crossref","first-page":"5361","DOI":"10.1021\/acs.jcim.2c00798","article-title":"ReLMole: molecular representation learning based on two-level graph similarities","volume":"62","author":"Ji","year":"2022","journal-title":"J Chem Inf Model"},{"key":"2023110801102730700_ref46","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1021\/ci010132r","article-title":"Reoptimization of MDL keys for use in drug discovery","volume":"42","author":"Durant","year":"2002","journal-title":"J Chem Inf Comput Sci"},{"key":"2023110801102730700_ref47","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J Chem Inf Model"},{"key":"2023110801102730700_ref48","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1039\/C8OB02193G","article-title":"Identifying a novel anticancer agent with microtubule-stabilizing effects through computational cell-based bioactivity prediction models and bioassays","volume":"17","author":"Luo","year":"2019","journal-title":"Org Biomol Chem"},{"key":"2023110801102730700_ref49","doi-asserted-by":"crossref","first-page":"6201","DOI":"10.1039\/C9OB00616H","article-title":"Discovery, biological evaluation, structure\u2013activity relationships and mechanism of action of pyrazolo [3, 4-b] pyridin-6-one derivatives as a new class of anticancer agents","volume":"17","author":"Guo","year":"2019","journal-title":"Org Biomol Chem"},{"key":"2023110801102730700_ref50","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1038\/nrd.2017.111","article-title":"Opportunities and challenges in phenotypic drug discovery: an industry perspective","volume":"16","author":"Moffat","year":"2017","journal-title":"Nat Rev Drug Discov"},{"key":"2023110801102730700_ref51","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1016\/j.drudis.2021.01.013","article-title":"Use of artificial intelligence to enhance phenotypic drug discovery","volume":"26","author":"Malandraki-Miller","year":"2021","journal-title":"Drug Discov Today"},{"key":"2023110801102730700_ref52","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.chembiol.2021.01.010","article-title":"The future of phenotypic drug discovery","volume":"28","author":"Berg","year":"2021","journal-title":"Cell Chem Biol"},{"key":"2023110801102730700_ref53","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1145\/3534678.3539426","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Li","year":"2022"},{"key":"2023110801102730700_ref54","doi-asserted-by":"crossref","first-page":"bbac303","DOI":"10.1093\/bib\/bbac303","article-title":"Attention-wise masked graph contrastive learning for predicting molecular property","volume":"23","author":"Liu","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023110801102730700_ref55","doi-asserted-by":"crossref","DOI":"10.1111\/j.1742-4658.2009.06929.x","article-title":"Cell biology, regulation and inhibition of \u03b2-secretase (BACE-1)[J]","volume":"276","author":"Hunt","year":"2009","journal-title":"FEBS J"},{"key":"2023110801102730700_ref56","doi-asserted-by":"crossref","first-page":"6314","DOI":"10.1021\/jm9006752","article-title":"Aminoimidazoles as potent and selective human \u03b2-secretase (BACE1) inhibitors","volume":"52","author":"Malamas","year":"2009","journal-title":"J Med Chem"},{"key":"2023110801102730700_ref57","article-title":"Infograph: unsupervised and semi-supervised graph-level representation learning via mutual information maximization","author":"Sun","year":"2019"},{"key":"2023110801102730700_ref58","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1145\/3394486.3403237","article-title":"GPT-GNN: generative pre-training of graph neural networks","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Hu","year":"2020"},{"key":"2023110801102730700_ref59","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2023110801102730700_ref60","article-title":"Strategies for pre-training graph neural networks","author":"Hu","year":"2019"},{"key":"2023110801102730700_ref61","first-page":"11548","article-title":"Self-supervised graph-level representation learning with local and global structure","author":"Xu","year":"2021","journal-title":"Int Conf Mach Learn"},{"key":"2023110801102730700_ref62","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","volume":"33","author":"Rong","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"2023110801102730700_ref63","first-page":"15920","article-title":"Adversarial graph augmentation to improve graph contrastive learning","volume":"34","author":"Suresh","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"2023110801102730700_ref64","first-page":"12121","article-title":"Graph contrastive learning automated","author":"You","year":"2021","journal-title":"Int Conf Mach Learn"},{"key":"2023110801102730700_ref65","first-page":"1070","article-title":"SimGRACE: a simple framework for graph contrastive learning without data augmentation","volume":"2022","author":"Xia","year":"2022","journal-title":"Proc ACM Web Confs"},{"key":"2023110801102730700_ref66","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"2023110801102730700_ref67","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1145\/3534678.3539321","article-title":"Graphmae: self-supervised masked graph autoencoders","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Hou","year":"2022"},{"key":"2023110801102730700_ref68","first-page":"15870","article-title":"Motif-based graph self-supervised learning for molecular property prediction","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/6\/bbad398\/52778061\/bbad398.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/6\/bbad398\/52778061\/bbad398.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T01:11:08Z","timestamp":1699405868000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad398\/7337693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,22]]},"references-count":68,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,9,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad398","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,11,1]]},"published":{"date-parts":[[2023,9,22]]},"article-number":"bbad398"}}