{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T21:31:02Z","timestamp":1773955862515,"version":"3.50.1"},"reference-count":91,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022YFF1203002"],"award-info":[{"award-number":["2022YFF1203002"]}]},{"name":"PharMolix Inc"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1109\/jbhi.2024.3505955","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T13:48:10Z","timestamp":1732542490000},"page":"981-992","source":"Crossref","is-referenced-by-count":24,"title":["BioMedGPT: An Open Multimodal Large Language Model for BioMedicine"],"prefix":"10.1109","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7107-378X","authenticated-orcid":false,"given":"Yizhen","family":"Luo","sequence":"first","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7722-6022","authenticated-orcid":false,"given":"Jiahuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6496-8761","authenticated-orcid":false,"given":"Siqi","family":"Fan","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4961-2715","authenticated-orcid":false,"given":"Kai","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1598-5981","authenticated-orcid":false,"given":"Massimo","family":"Hong","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]},{"given":"Yushuai","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8291-049X","authenticated-orcid":false,"given":"Mu","family":"Qiao","sequence":"additional","affiliation":[{"name":"PharMolix Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1134-2343","authenticated-orcid":false,"given":"Zaiqing","family":"Nie","sequence":"additional","affiliation":[{"name":"PharMolix Inc., Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Gpt-4 technical report","author":"Achiam","year":"2023"},{"key":"ref2","article-title":"Llama: Open and efficient foundation language models","author":"Touvron","year":"2023"},{"issue":"240","key":"ref3","first-page":"1","article-title":"Palm: Scaling language modeling with pathways","volume":"24","author":"Chowdhery","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"ref4","article-title":"Finetuned language models are zero-shot learners","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wei","year":"2021"},{"key":"ref5","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-024-00832-8"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.34133\/energymatadv.0026"},{"issue":"07","key":"ref8","article-title":"Guiding ai with human intuition for solving mathematical problems in chat GPT","volume":"11","author":"Poola","year":"2023"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1126\/science.287.5460.1960"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/nrd2082"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp630"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/nature19946"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1371"},{"key":"ref14","article-title":"The impact of large language models on scientific discovery: A preliminary study using gpt-4","author":"AI4Science","year":"2023"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-28494-3"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-023-05411-z"},{"key":"ref17","article-title":"Improving language understanding by generative pre-training","author":"Radford","year":"2018"},{"issue":"1","key":"ref18","first-page":"1","article-title":"Pubmed: The bibliographic database","volume":"2","author":"Canese","year":"2013","journal-title":"NCBI Handbook"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03185-2"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1525\/9780520940420-020"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocae045"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.447"},{"key":"ref23","first-page":"19730","article-title":"Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li","year":"2023"},{"key":"ref24","article-title":"Visual instruction tuning","volume":"36","author":"Liu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref25","article-title":"A molecular multimodal foundation model associating molecule graphs with natural language","author":"Su","year":"2022"},{"key":"ref26","first-page":"30458","article-title":"Enhancing activity prediction models in drug discovery with the ability to understand human language","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Seidl","year":"2023"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00759-6"},{"key":"ref28","first-page":"38749","article-title":"Protst: Multi-modality learning of protein sequences and biomedical texts","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu","year":"2023"},{"key":"ref29","article-title":"Molfm: A multimodal molecular foundation model","author":"Luo","year":"2023"},{"key":"ref30","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"Khosla","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref31","article-title":"Galactica: A large language model for science","author":"Taylor","year":"2022"},{"key":"ref32","article-title":"Mol-instructions: A large-scale biomolecular instruction dataset for large language models","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Fang","year":"2024"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.70"},{"key":"ref34","article-title":"Protchatgpt: Towards understanding proteins with large language models","author":"Wang","year":"2024"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/tai.2025.3564914"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01282"},{"key":"ref37","article-title":"Llama 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"key":"ref38","article-title":"Pre-training molecular graph representation with 3D geometry","volume-title":"Proc. ICLR 2022 Workshop Geometrical Topological Representation Learn.","author":"Liu","year":"2022"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1126\/science.ade2574"},{"key":"ref40","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref41","article-title":"Mistral 7b","author":"Jiang","year":"2023"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202306724"},{"key":"ref43","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","volume":"33","author":"Lewis","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1115\/1.4063843"},{"key":"ref45","article-title":"A survey on knowledge distillation of large language models","author":"Xu","year":"2024"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ad7228"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1063\/5.0203126"},{"key":"ref48","article-title":"Outrageously large neural networks: The sparsely-gated mixture-of-experts layer","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Shazeer","year":"2016"},{"key":"ref49","article-title":"Lora: Low-rank adaptation of large language models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hu","year":"2021"},{"key":"ref50","article-title":"Towards expert-level medical question answering with large language models","author":"Singhal","year":"2023"},{"key":"ref51","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Machi. Learn.","author":"Radford","year":"2021"},{"key":"ref52","first-page":"12888","article-title":"Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li","year":"2022"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00759-6"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.26"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.116"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108073"},{"key":"ref57","article-title":"3D-MoLM: Towards 3D Molecule-Text Interpretation in Language Models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2024"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-025-02636-z"},{"key":"ref59","article-title":"Proteinchat: Towards achieving chatgpt-like functionalities on protein 3 d structures","author":"Guo","year":"2023","journal-title":"Authorea Preprints"},{"key":"ref60","article-title":"How powerful are graph neural networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","year":"2018"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.98.2.381"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.14778\/3611540.3611569"},{"key":"ref63","article-title":"Training deep nets with sublinear memory cost","author":"Chen","year":"2016"},{"key":"ref64","first-page":"16344","article-title":"Flashattention: Fast and memory-efficient exact attention with io-awareness","volume":"35","author":"Dao","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv951"},{"key":"ref66","first-page":"1","article-title":"RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling","volume":"8","author":"Landrum","year":"2013","journal-title":"Greg Landrum"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gku989"},{"key":"ref68","article-title":"Introducing chatgpt","year":"2022"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1259"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.3390\/app11146421"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00447-x"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073135"},{"key":"ref73","first-page":"74","article-title":"Rouge: A package for automatic evaluation of summaries","volume-title":"Proc. Text Summarization Branches Out","author":"Lin","year":"2004"},{"key":"ref74","first-page":"65","article-title":"Meteor: An automatic metric for MT evaluation with improved correlation with human judgments","volume-title":"Proc. ACL Workshop Intrinsic Extrinsic Eval. Measures Mach. Transl. And\/Or Summarization","author":"Banerjee","year":"2005"},{"key":"ref75","first-page":"22199","article-title":"Large language models are zero-shot reasoners","volume":"35","author":"Kojima","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref76","article-title":"Llama 3 model card","year":"2024"},{"key":"ref77","first-page":"41987","article-title":"Cell cycle-regulated gene expression inarabidopsis","volume-title":"J. Biol. Chem.","volume":"277","author":"Menges","year":"2002"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1128\/IAI.73.10.6407-6418.2005"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0321-8"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.3988"},{"key":"ref81","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-025-01011-z"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1162\/coli.a.16"},{"key":"ref84","article-title":"Red-teaming large language models using chain of utterances for safety-alignment","author":"Bhardwaj","year":"2023"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00939-z"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-short.138"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.966"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.71"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3393356"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3095381"},{"key":"ref91","first-page":"35156","article-title":"Peer: A comprehensive and multi-task benchmark for protein sequence understanding","volume":"35","author":"Xu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6221020\/11372623\/10767279.pdf?arnumber=10767279","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:07:22Z","timestamp":1770671242000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10767279\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":91,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2024.3505955","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]}}}