{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T14:49:52Z","timestamp":1792162192186,"version":"4.4.0"},"reference-count":150,"publisher":"American Chemical Society (ACS)","issue":"10","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC2100900"],"award-info":[{"award-number":["2021YFC2100900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009592","name":"Beijing Municipal Science and Technology Commission, Adminitrative Commission of Zhongguancun Science Park","doi-asserted-by":"publisher","award":["Z251100000125009"],"award-info":[{"award-number":["Z251100000125009"]}],"id":[{"id":"10.13039\/501100009592","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004147","name":"Tsinghua University","doi-asserted-by":"publisher","award":["20223080016"],"award-info":[{"award-number":["20223080016"]}],"id":[{"id":"10.13039\/501100004147","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21938004"],"award-info":[{"award-number":["21938004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22078172"],"award-info":[{"award-number":["22078172"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The rapid growth of genomic and metagenomic data has created a large gap between protein sequence discovery and enzyme functional characterization. Deep learning has become an important tool for enzyme-related prediction tasks, including EC and GO annotation, substrate specificity, catalytic-residue identification, kinetic-parameter estimation, thermostability, pH optima, and candidate discovery. This review summarizes recent progress with an emphasis on benchmark design rather than reported scores alone. We compare representative methods by input modality, training data, split strategy, leakage control, evaluation metric, and validation evidence. Across tasks, apparent performance gains can arise from dataset scale, annotation quality, homolog overlap, negative sampling, or endpoint definition rather than architecture alone. Current models are valuable for annotation and candidate prioritization, but robust discovery of remote homologs, rare functions, or new catalytic activities still requires low-identity or out-of-distribution evaluation and experimental validation.<\/jats:p>","DOI":"10.1021\/acssynbio.6c00633","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T18:02:28Z","timestamp":1788544948000},"page":"3937-3958","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning in Enzyme Function Prediction and Novel Enzyme Discovery"],"prefix":"10.1021","volume":"15","author":[{"given":"Rongsheng","family":"Gao","sequence":"first","affiliation":[{"name":"Tsinghua University , , ,","place":["Beijing, China, 100084"]},{"name":"Tsinghua University , , ,","place":["Beijing, China, 100084"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youmeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University , , ,","place":["Beijing, China, 100084"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2615-9817","authenticated-orcid":true,"given":"Chengye","family":"Duan","sequence":"additional","affiliation":[{"name":"Tsinghua University , , ,","place":["Beijing, China, 100084"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5948-1493","authenticated-orcid":true,"given":"Nan","family":"Qin","sequence":"additional","affiliation":[{"name":"Tsinghua University , , ,","place":["Beijing, China, 100084"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kunyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University , , ,","place":["Beijing, China, 100084"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3249-030X","authenticated-orcid":true,"given":"Tatsuhisa","family":"Tsuboi","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School , , ,","place":["Shenzhen, China, 518055"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3792-5544","authenticated-orcid":true,"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University , , ,","place":["Beijing, China, 100084"]},{"name":"Tsinghua University , , ,","place":["Beijing, China, 100084"]},{"name":"Tsinghua University , , ,","place":["Beijing, China, 100084"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"issue":"D1","key":"2026101610062643000_cit1","doi-asserted-by":"publisher","first-page":"D609","DOI":"10.1093\/nar\/gkae1010","article-title":"UniProt: the Universal protein knowledgebase in 2025","volume":"53","author":"Bateman","year":"2025","journal-title":"Nucleic Acids Res."},{"key":"2026101610062643000_cit2","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3389\/fgene.2015.00119","article-title":"Annotation and curation of uncharacterized proteins-challenges","volume":"6","author":"Ijaq","year":"2015","journal-title":"Front. Genet."},{"issue":"2","key":"2026101610062643000_cit3","article-title":"Enzymes as drugs: an overview","volume":"3","author":"Kaur","year":"2012","journal-title":"Indian J. Pharm. Educ. Res."},{"issue":"17","key":"2026101610062643000_cit4","doi-asserted-by":"publisher","first-page":"9181","DOI":"10.3390\/ijms22179181","article-title":"Enzyme therapy: current challenges and future perspectives","volume":"22","author":"De La Fuente","year":"2021","journal-title":"Int. J. Mol. Sci."},{"issue":"4","key":"2026101610062643000_cit5","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1002\/bab.1919","article-title":"Enzymes for pharmaceutical and therapeutic applications","volume":"67","author":"Meghwanshi","year":"2020","journal-title":"Biotechnol. Appl. Biochem."},{"key":"2026101610062643000_cit6","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/B978-0-444-63453-5.00015-X","article-title":"Industrial enzymes","volume-title":"Industrial biorefineries & white biotechnology","author":"Singhania","year":"2015"},{"issue":"6","key":"2026101610062643000_cit7","doi-asserted-by":"publisher","first-page":"238","DOI":"10.3390\/catal8060238","article-title":"Industrial applications of enzymes: Recent advances, techniques, and outlooks","volume":"8","author":"Chapman","year":"2018","journal-title":"Catalysts"},{"issue":"1","key":"2026101610062643000_cit8","doi-asserted-by":"publisher","first-page":"011501","DOI":"10.1063\/1.4997367","article-title":"Engineering of thermostable enzymes for industrial applications","volume":"2","author":"Rigoldi","year":"2018","journal-title":"APL Bioeng."},{"key":"2026101610062643000_cit9","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-13-0785-0","volume-title":"Enzymes: Catalysis, Kinetics and Mechanisms","author":"Punekar","year":"2018"},{"issue":"5","key":"2026101610062643000_cit10","doi-asserted-by":"publisher","first-page":"3033","DOI":"10.1021\/acscatal.1c05800","article-title":"Substrate-binding mode of a thermophilic PET hydrolase and engineering the enzyme to enhance the hydrolytic efficacy","volume":"12","author":"Zeng","year":"2022","journal-title":"ACS Catal."},{"issue":"7","key":"2026101610062643000_cit11","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1002\/jobm.202100107","article-title":"Microbial halophilic lipases: A review","volume":"61","author":"Qiu","year":"2021","journal-title":"J. Basic Microbiol."},{"key":"2026101610062643000_cit12","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/978-1-62703-293-3_15","article-title":"Promiscuity-based enzyme selection for rational directed evolution experiments","author":"Chakraborty","year":"2013","journal-title":"Enzyme engineering: methods and protocols"},{"issue":"5","key":"2026101610062643000_cit13","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1016\/j.cbpa.2006.08.011","article-title":"Enzyme promiscuity: evolutionary and mechanistic aspects","volume":"10","author":"Khersonsky","year":"2006","journal-title":"Curr. Opin. Chem. Biol."},{"issue":"6639","key":"2026101610062643000_cit14","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1126\/science.adf2465","article-title":"Enzyme function prediction using contrastive learning","volume":"379","author":"Yu","year":"2023","journal-title":"Science"},{"issue":"3","key":"2026101610062643000_cit15","doi-asserted-by":"publisher","first-page":"bbae196","DOI":"10.1093\/bib\/bbae196","article-title":"DeepSS2GO: protein function prediction from secondary structure","volume":"25","author":"Song","year":"2024","journal-title":"Briefings Bioinf."},{"issue":"1","key":"2026101610062643000_cit16","doi-asserted-by":"publisher","first-page":"2737","DOI":"10.1038\/s41467-025-57324-5","article-title":"TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor","volume":"16","author":"van der Weg","year":"2025","journal-title":"Nat. Commun."},{"issue":"1","key":"2026101610062643000_cit17","doi-asserted-by":"publisher","first-page":"bbad506","DOI":"10.1093\/bib\/bbad506","article-title":"DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates","volume":"25","author":"Qiu","year":"2024","journal-title":"Briefings Bioinf."},{"key":"2026101610062643000_cit18","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.ymeth.2025.02.010","article-title":"A novel interpretability framework for enzyme turnover number prediction boosted by pre-trained enzyme embeddings and adaptive gate network","volume":"237","author":"Du","year":"2025","journal-title":"Methods"},{"issue":"14","key":"2026101610062643000_cit19","doi-asserted-by":"publisher","first-page":"4668","DOI":"10.1021\/acs.jctc.2c01227","article-title":"Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks","volume":"19","author":"Ramirez-Palacios","year":"2023","journal-title":"J. Chem. Theory Comput."},{"issue":"3","key":"2026101610062643000_cit20","doi-asserted-by":"publisher","first-page":"btaf053","DOI":"10.1093\/bioinformatics\/btaf053","article-title":"DeepES: deep learning-based enzyme screening to identify orphan enzyme genes","volume":"41","author":"Hirota","year":"2025","journal-title":"Bioinformatics"},{"key":"2026101610062643000_cit21","doi-asserted-by":"publisher","first-page":"1446063","DOI":"10.3389\/frai.2024.1446063","article-title":"Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost","volume":"7","author":"Alazmi","year":"2024","journal-title":"Frontiers in Artificial Intelligence"},{"issue":"12","key":"2026101610062643000_cit22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000605","article-title":"Annotation error in public databases: misannotation of molecular function in enzyme superfamilies","volume":"5","author":"Schnoes","year":"2009","journal-title":"PLoS Comput. Biol."},{"issue":"3","key":"2026101610062643000_cit23","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1038\/nmeth.2340","article-title":"A large-scale evaluation of computational protein function prediction","volume":"10","author":"Radivojac","year":"2013","journal-title":"Nat. Methods"},{"key":"2026101610062643000_cit24","doi-asserted-by":"publisher","first-page":"arXiv:2503.16659","DOI":"10.48550\/arXiv.2503.16659","article-title":"Advances in Protein Representation Learning: Methods, Applications, and Future Directions","author":"Nguyen","year":"2025","journal-title":"arXiv"},{"issue":"6","key":"2026101610062643000_cit25","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.cels.2021.05.017","article-title":"Learning the protein language: Evolution, structure, and function","volume":"12","author":"Bepler","year":"2021","journal-title":"Cell Syst."},{"issue":"1","key":"2026101610062643000_cit26","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s13040-025-00457-6","article-title":"Recent advances in deep learning for protein-protein interaction: a review","volume":"18","author":"Cui","year":"2025","journal-title":"BioData Min."},{"key":"2026101610062643000_cit27","doi-asserted-by":"publisher","first-page":"101010","DOI":"10.1016\/j.cogsc.2025.101010","article-title":"Generative artificial intelligence for enzyme design: Recent advances in models and applications","volume":"52","author":"Wen","year":"2025","journal-title":"Curr. Opin. Green Sustainable Chem."},{"issue":"1","key":"2026101610062643000_cit28","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1021\/acscatal.5c04814","article-title":"Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives","volume":"16","author":"Moorhoff","year":"2025","journal-title":"ACS Catal."},{"key":"2026101610062643000_cit29","doi-asserted-by":"publisher","first-page":"101208","DOI":"10.1016\/j.coche.2025.101208","article-title":"Enzyme property prediction using artificial intelligence","volume":"51","author":"Yuan","year":"2026","journal-title":"Curr. Opin. Chem. Eng."},{"issue":"Web Server","key":"2026101610062643000_cit30","doi-asserted-by":"publisher","first-page":"W6","DOI":"10.1093\/nar\/gkl164","article-title":"BLAST: improvements for better sequence analysis","volume":"34","author":"Ye","year":"2006","journal-title":"Nucleic Acids Res."},{"issue":"17","key":"2026101610062643000_cit31","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","article-title":"Gapped BLAST and PSI-BLAST: a new generation of protein database search programs","volume":"25","author":"Altschul","year":"1997","journal-title":"Nucleic Acids Res."},{"issue":"suppl","key":"2026101610062643000_cit32","doi-asserted-by":"publisher","first-page":"W29","DOI":"10.1093\/nar\/gkr367","article-title":"HMMER web server: interactive sequence similarity searching","volume":"39","author":"Finn","year":"2011","journal-title":"Nucleic Acids Res."},{"issue":"2","key":"2026101610062643000_cit33","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/nmeth.2803","article-title":"EC-BLAST: a tool to automatically search and compare enzyme reactions","volume":"11","author":"Rahman","year":"2014","journal-title":"Nat. Methods"},{"key":"2026101610062643000_cit34","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1186\/1471-2105-6-272","article-title":"Automated methods of predicting the function of biological sequences using GO and BLAST","volume":"6","author":"Jones","year":"2005","journal-title":"BMC Bioinf."},{"issue":"W1","key":"2026101610062643000_cit35","doi-asserted-by":"publisher","first-page":"W115","DOI":"10.1093\/nar\/gkad328","article-title":"dbCAN3: automated carbohydrate-active enzyme and substrate annotation","volume":"51","author":"Zheng","year":"2023","journal-title":"Nucleic Acids Res."},{"issue":"5","key":"2026101610062643000_cit36","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000063","article-title":"Sequence similarity network reveals common ancestry of multidomain proteins","volume":"4","author":"Song","year":"2008","journal-title":"PLoS Comput. Biol."},{"issue":"11","key":"2026101610062643000_cit37","doi-asserted-by":"publisher","first-page":"6073","DOI":"10.1073\/pnas.95.11.6073","article-title":"Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships","volume":"95","author":"Brenner","year":"1998","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"36","key":"2026101610062643000_cit38","doi-asserted-by":"publisher","first-page":"13474","DOI":"10.1073\/pnas.0803860105","article-title":"Phylogenetic profiles reveal evolutionary relationships within the \u201ctwilight zone\u201d of sequence similarity","volume":"105","author":"Chang","year":"2008","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"W1","key":"2026101610062643000_cit39","doi-asserted-by":"publisher","first-page":"W296","DOI":"10.1093\/nar\/gky427","article-title":"SWISS-MODEL: homology modelling of protein structures and complexes","volume":"46","author":"Waterhouse","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"2026101610062643000_cit40","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/1471-2105-9-40","article-title":"I-TASSER server for protein 3D structure prediction","volume":"9","author":"Zhang","year":"2008","journal-title":"BMC Bioinf."},{"issue":"9","key":"2026101610062643000_cit41","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1038\/s41594-021-00650-1","article-title":"AlphaFold2 and the future of structural biology","volume":"28","author":"Cramer","year":"2021","journal-title":"Nat. Struct. Mol. Biol."},{"key":"2026101610062643000_cit42","first-page":"1000","volume-title":"Using AutoDock 4 and AutoDock vina with AutoDockTools: a tutorial","author":"Huey","year":"2012"},{"issue":"1","key":"2026101610062643000_cit43","doi-asserted-by":"publisher","first-page":"5.5","DOI":"10.1002\/0471250953.bi0505s14","article-title":"Using Dali for structural comparison of proteins","volume":"14","author":"Holm","year":"2006","journal-title":"Curr. Protoc. Bioinform."},{"issue":"7","key":"2026101610062643000_cit44","doi-asserted-by":"publisher","first-page":"2302","DOI":"10.1093\/nar\/gki524","article-title":"TM-align: a protein structure alignment algorithm based on the TM-score","volume":"33","author":"Zhang","year":"2005","journal-title":"Nucleic Acids Res."},{"issue":"Web Server","key":"2026101610062643000_cit45","doi-asserted-by":"publisher","first-page":"W582","DOI":"10.1093\/nar\/gkh430","article-title":"FATCAT: a web server for flexible structure comparison and structure similarity searching","volume":"32","author":"Ye","year":"2004","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"2026101610062643000_cit46","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/s1367-5931(02)00003-0","article-title":"Domains, motifs and clusters in the protein universe","volume":"7","author":"Liu","year":"2003","journal-title":"Curr. Opin. Chem. Biol."},{"issue":"suppl_1","key":"2026101610062643000_cit47","doi-asserted-by":"publisher","first-page":"i26","DOI":"10.1093\/bioinformatics\/btg1002","article-title":"Remote homology detection: a motif based approach","volume":"19","author":"Ben-Hur","year":"2003","journal-title":"Bioinformatics"},{"issue":"D1","key":"2026101610062643000_cit48","doi-asserted-by":"publisher","first-page":"D279","DOI":"10.1093\/nar\/gkv1344","article-title":"The Pfam protein families database: towards a more sustainable future","volume":"44","author":"Finn","year":"2016","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"2026101610062643000_cit49","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1093\/nar\/28.1.225","article-title":"PRINTS-S: the database formerly known as PRINTS","volume":"28","author":"Attwood","year":"2000","journal-title":"Nucleic Acids Res."},{"issue":"suppl_1","key":"2026101610062643000_cit50","doi-asserted-by":"publisher","first-page":"D161","DOI":"10.1093\/nar\/gkp885","article-title":"PROSITE, a protein domain database for functional characterization and annotation","volume":"38","author":"Sigrist","year":"2010","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"2026101610062643000_cit51","doi-asserted-by":"publisher","first-page":"D242","DOI":"10.1093\/nar\/gkr1064","article-title":"ELM\ue5f8the database of eukaryotic linear motifs","volume":"40","author":"Dinkel","year":"2012","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"2026101610062643000_cit52","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1002\/prot.21092","article-title":"Comparison of protein active site structures for functional annotation of proteins and drug design","volume":"65","author":"Powers","year":"2006","journal-title":"Proteins:Struct., Funct., Bioinf."},{"issue":"11","key":"2026101610062643000_cit53","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.3390\/genes11111264","article-title":"Automatic gene function prediction in the 2020\u2019s","volume":"11","author":"Makrodimitris","year":"2020","journal-title":"Genes"},{"issue":"20","key":"2026101610062643000_cit54","doi-asserted-by":"publisher","first-page":"5912","DOI":"10.1021\/jm050362n","article-title":"A critical assessment of docking programs and scoring functions","volume":"49","author":"Warren","year":"2006","journal-title":"J. Med. Chem."},{"issue":"15","key":"2026101610062643000_cit55","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"2026101610062643000_cit56","doi-asserted-by":"publisher","first-page":"2021.09.20.461077","DOI":"10.1101\/2021.09.20.461077","article-title":"ProteInfer: deep networks for protein functional inference","volume":"2021","author":"Sanderson","year":"2021"},{"key":"2026101610062643000_cit57","doi-asserted-by":"publisher","first-page":"arXiv:1910.14659","DOI":"10.48550\/arXiv.1910.14659","article-title":"Masked language model scoring","author":"Salazar","year":"2019","journal-title":"arXiv"},{"issue":"27","key":"2026101610062643000_cit58","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2311887121","article-title":"Pairing interacting protein sequences using masked language modeling","volume":"121","author":"Lupo","year":"2024","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"2026101610062643000_cit59","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1016\/j.csbj.2021.03.022","article-title":"The language of proteins: NLP, machine learning & protein sequences","volume":"19","author":"Ofer","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"2026101610062643000_cit60","doi-asserted-by":"crossref","DOI":"10.1101\/676825","article-title":"Evaluating Protein Transfer Learning with TAPE","volume-title":"33rd Conference on Neural Information Processing Systems (NeurIPS)","author":"Rao","year":"2019"},{"issue":"10","key":"2026101610062643000_cit61","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/tpami.2021.3095381","article-title":"ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning","volume":"44","author":"Elnaggar","year":"2022","journal-title":"Ieee Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"6637","key":"2026101610062643000_cit62","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"key":"2026101610062643000_cit63","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1126\/science.ads0018","article-title":"Simulating 500 million years of evolution with a language model","volume":"387","author":"Hayes","year":"2025","journal-title":"Science"},{"issue":"12","key":"2026101610062643000_cit64","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1038\/s41592-019-0598-1","article-title":"Unified rational protein engineering with sequence-based deep representation learning","volume":"16","author":"Alley","year":"2019","journal-title":"Nat. Methods"},{"issue":"1","key":"2026101610062643000_cit65","doi-asserted-by":"publisher","first-page":"4348","DOI":"10.1038\/s41467-022-32007-7","article-title":"ProtGPT2 is a deep unsupervised language model for protein design","volume":"13","author":"Ferruz","year":"2022","journal-title":"Nat. Commun."},{"issue":"1","key":"2026101610062643000_cit66","doi-asserted-by":"publisher","first-page":"bpae043","DOI":"10.1093\/biomethods\/bpae043","article-title":"Multimodal pretraining for unsupervised protein representation learning","volume":"9","author":"Nguyen","year":"2024","journal-title":"Biology Methods and Protocols"},{"issue":"23","key":"2026101610062643000_cit67","doi-asserted-by":"publisher","first-page":"2001","DOI":"10.1002\/jcc.27388","article-title":"GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity","volume":"45","author":"Boonyarit","year":"2024","journal-title":"J. Comput. Chem."},{"key":"2026101610062643000_cit68","doi-asserted-by":"crossref","unstructured":"Rao, R. M.; Liu, J.; Verkuil, R.; Meier, J.; Canny, J.; Abbeel, P.; Sercu, T.; Rives, A.\n          MSA transformer. In International Conference on Machine Learning; PMLR, 2021; pp 8844\u22128856.","DOI":"10.1101\/2021.02.12.430858"},{"key":"2026101610062643000_cit69","doi-asserted-by":"publisher","first-page":"2024.09.25.614868","DOI":"10.1101\/2024.09.25.614868","article-title":"Modeling protein-small molecule conformational ensembles with ChemNet","author":"Anishchenko","year":"2024","journal-title":"bioRxiv"},{"issue":"1","key":"2026101610062643000_cit70","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1186\/s12859-025-06081-9","article-title":"Comparative Assessment of Protein Large Language Models for Enzyme Commission Number Prediction","volume":"26","author":"Capela","year":"2025","journal-title":"BMC Bioinf."},{"issue":"1","key":"2026101610062643000_cit71","doi-asserted-by":"publisher","first-page":"5566","DOI":"10.1038\/s41467-024-49798-6","article-title":"Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning","volume":"15","author":"Zhou","year":"2024","journal-title":"Nat. Commun."},{"issue":"1","key":"2026101610062643000_cit72","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1021\/acs.jctc.4c01391","article-title":"BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions","volume":"21","author":"Wang","year":"2024","journal-title":"J. Chem. Theory Comput."},{"issue":"1","key":"2026101610062643000_cit73","doi-asserted-by":"publisher","first-page":"bbac619","DOI":"10.1093\/bib\/bbac619","article-title":"Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies","volume":"24","author":"Yeung","year":"2023","journal-title":"Briefings Bioinf."},{"issue":"D1","key":"2026101610062643000_cit74","doi-asserted-by":"publisher","first-page":"D498","DOI":"10.1093\/nar\/gkaa1025","article-title":"BRENDA, the ELIXIR core data resource in 2021: new developments and updates","volume":"49","author":"Chang","year":"2021","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"2026101610062643000_cit75","doi-asserted-by":"publisher","first-page":"D656","DOI":"10.1093\/nar\/gkx1065","article-title":"SABIO-RK: an updated resource for manually curated biochemical reaction kinetics","volume":"46","author":"Wittig","year":"2018","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"2026101610062643000_cit76","doi-asserted-by":"publisher","first-page":"D643","DOI":"10.1093\/nar\/gkaf1082","article-title":"Open Enzyme Database: a community-wide repository for sharing enzyme data","volume":"54","author":"Yuan","year":"2026","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"2026101610062643000_cit77","doi-asserted-by":"publisher","first-page":"D358","DOI":"10.1093\/nar\/gkaf1226","article-title":"AlphaFold Protein Structure Database 2025: a redesigned interface and updated structural coverage","volume":"54","author":"Bertoni","year":"2026","journal-title":"Nucleic Acids Res."},{"issue":"28","key":"2026101610062643000_cit78","doi-asserted-by":"publisher","first-page":"13996","DOI":"10.1073\/pnas.1821905116","article-title":"Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers","volume":"116","author":"Ryu","year":"2019","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"issue":"5","key":"2026101610062643000_cit79","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1093\/bioinformatics\/btx680","article-title":"DEEPre: sequence-based enzyme EC number prediction by deep learning","volume":"34","author":"Li","year":"2018","journal-title":"Bioinformatics"},{"issue":"1","key":"2026101610062643000_cit80","doi-asserted-by":"publisher","first-page":"7370","DOI":"10.1038\/s41467-023-43216-z","article-title":"Functional annotation of enzyme-encoding genes using deep learning with transformer layers","volume":"14","author":"Kim","year":"2023","journal-title":"Nat. Commun."},{"issue":"4","key":"2026101610062643000_cit81","doi-asserted-by":"publisher","first-page":"bbae225","DOI":"10.1093\/bib\/bbae225","article-title":"ifDEEPre: large protein language-based deep learning enables interpretable and fast predictions of enzyme commission numbers","volume":"25","author":"Tan","year":"2024","journal-title":"Briefings Bioinf."},{"issue":"6","key":"2026101610062643000_cit82","doi-asserted-by":"publisher","first-page":"3691","DOI":"10.1109\/tcbb.2023.3311427","article-title":"A transformer based deep learning system for accurate annotation of enzyme commission numbers","volume":"20","author":"Tamir","year":"2023","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"1","key":"2026101610062643000_cit83","doi-asserted-by":"publisher","first-page":"8180","DOI":"10.1038\/s41467-024-52533-w","article-title":"Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures","volume":"15","author":"Song","year":"2024","journal-title":"Nat. Commun."},{"key":"2026101610062643000_cit84","doi-asserted-by":"publisher","first-page":"bbaf476","DOI":"10.1093\/bib\/bbaf476","article-title":"Autoregressive Enzyme Function Prediction with Multi-scale Multi-modality Fusion","volume":"26","author":"Rong","year":"2024","journal-title":"Brief. Bioinform."},{"issue":"1","key":"2026101610062643000_cit85","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1038\/s42003-024-07359-z","article-title":"Improved enzyme functional annotation prediction using contrastive learning with structural inference","volume":"7","author":"Yang","year":"2024","journal-title":"Commun. Biol."},{"issue":"1","key":"2026101610062643000_cit86","doi-asserted-by":"publisher","first-page":"bbad401","DOI":"10.1093\/bib\/bbad401","article-title":"Evidential deep learning for trustworthy prediction of enzyme commission number","volume":"25","author":"Han","year":"2023","journal-title":"Briefings Bioinf."},{"issue":"6","key":"2026101610062643000_cit87","doi-asserted-by":"publisher","first-page":"2604","DOI":"10.1109\/tcbb.2024.3447037","article-title":"Parallel Convolutional Contrastive Learning Method for Enzyme Function Prediction","volume":"21","author":"Yu","year":"2024","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"5","key":"2026101610062643000_cit88","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1012135","article-title":"Leveraging conformal prediction to annotate enzyme function space with limited false positives","volume":"20","author":"Ding","year":"2024","journal-title":"PLoS Comput. Biol."},{"issue":"1","key":"2026101610062643000_cit89","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s13321-024-00827-y","article-title":"A general model for predicting enzyme functions based on enzymatic reactions","volume":"16","author":"Qian","year":"2024","journal-title":"J. Cheminf."},{"key":"2026101610062643000_cit90","doi-asserted-by":"publisher","first-page":"0153","DOI":"10.34133\/research.0153","article-title":"Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework","volume":"6","author":"Shi","year":"2023","journal-title":"Research"},{"issue":"18","key":"2026101610062643000_cit91","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1093\/bioinformatics\/btab198","article-title":"TALE: Transformer-based protein function Annotation with joint sequence\u2212Label Embedding","volume":"37","author":"Cao","year":"2021","journal-title":"Bioinformatics"},{"issue":"W1","key":"2026101610062643000_cit92","doi-asserted-by":"publisher","first-page":"W469","DOI":"10.1093\/nar\/gkab398","article-title":"NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information","volume":"49","author":"Yao","year":"2021","journal-title":"Nucleic Acids Res."},{"issue":"Supplement_1","key":"2026101610062643000_cit93","doi-asserted-by":"publisher","first-page":"i238","DOI":"10.1093\/bioinformatics\/btac256","article-title":"DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms","volume":"38","author":"Kulmanov","year":"2022","journal-title":"Bioinformatics"},{"issue":"3","key":"2026101610062643000_cit94","doi-asserted-by":"publisher","first-page":"btad094","DOI":"10.1093\/bioinformatics\/btad094","article-title":"PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships","volume":"39","author":"Pan","year":"2023","journal-title":"Bioinformatics"},{"issue":"1","key":"2026101610062643000_cit95","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1186\/s13059-024-03166-1","article-title":"AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding","volume":"25","author":"Zheng","year":"2024","journal-title":"Genome Biol."},{"key":"2026101610062643000_cit96","doi-asserted-by":"publisher","first-page":"arXiv:2412.05776","DOI":"10.48550\/arXiv.2412.05776","article-title":"A Transformer Based Fusion Model for Accurately Predicting Gene Ontology (GO) Terms from Full scale Protein Sequences","author":"Tamir","year":"2024","journal-title":"arXiv"},{"key":"2026101610062643000_cit97","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.80942","article-title":"ProteInfer, deep neural networks for protein functional inference","volume":"12","author":"Sanderson","year":"2023","journal-title":"eLife"},{"issue":"8","key":"2026101610062643000_cit98","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1093\/bioinformatics\/btac104","article-title":"TransformerGO: predicting protein\u2212protein interactions by modelling the attention between sets of gene ontology terms","volume":"38","author":"Ieremie","year":"2022","journal-title":"Bioinformatics"},{"issue":"2","key":"2026101610062643000_cit99","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1093\/bioinformatics\/btz595","article-title":"DeepGOPlus: improved protein function prediction from sequence","volume":"36","author":"Kulmanov","year":"2020","journal-title":"Bioinformatics"},{"issue":"Supplement_1","key":"2026101610062643000_cit100","doi-asserted-by":"publisher","first-page":"i262","DOI":"10.1093\/bioinformatics\/btab270","article-title":"DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction","volume":"37","author":"You","year":"2021","journal-title":"Bioinformatics"},{"issue":"1","key":"2026101610062643000_cit101","doi-asserted-by":"publisher","first-page":"2787","DOI":"10.1038\/s41467-023-38347-2","article-title":"A general model to predict small molecule substrates of enzymes based on machine and deep learning","volume":"14","author":"Kroll","year":"2023","journal-title":"Nat. Commun."},{"issue":"5","key":"2026101610062643000_cit102","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1012100","article-title":"A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships","volume":"20","author":"Kroll","year":"2024","journal-title":"PLoS Comput. Biol."},{"issue":"1","key":"2026101610062643000_cit103","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1021\/acs.jcim.4c01801","article-title":"Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery","volume":"65","author":"Qian","year":"2024","journal-title":"J. Chem. Inf. Model."},{"issue":"48","key":"2026101610062643000_cit104","doi-asserted-by":"publisher","first-page":"14229","DOI":"10.1039\/d3sc02048g","article-title":"EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions","volume":"14","author":"Heid","year":"2023","journal-title":"Chem. Sci."},{"issue":"9","key":"2026101610062643000_cit105","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1021\/acs.chemrestox.4c00199","article-title":"Prediction of Cytochrome P450 Substrates Using the Explainable Multitask Deep Learning Models","volume":"37","author":"Fang","year":"2024","journal-title":"Chem. Res. Toxicol."},{"issue":"8","key":"2026101610062643000_cit106","doi-asserted-by":"publisher","first-page":"3149","DOI":"10.1021\/acs.jcim.4c00115","article-title":"DeepP450: Predicting Human P450 Activities of Small Molecules by Integrating Pretrained Protein Language Model and Molecular Representation","volume":"64","author":"Chang","year":"2024","journal-title":"J. Chem. Inf. Model."},{"issue":"7","key":"2026101610062643000_cit107","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.1021\/acs.jcim.3c01396","article-title":"Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition","volume":"64","author":"Chen","year":"2023","journal-title":"J. Chem. Inf. Model."},{"issue":"2","key":"2026101610062643000_cit108","doi-asserted-by":"publisher","first-page":"btad046","DOI":"10.1093\/bioinformatics\/btad046","article-title":"Phosformer: an explainable transformer model for protein kinase-specific phosphorylation predictions","volume":"39","author":"Zhou","year":"2023","journal-title":"Bioinformatics"},{"issue":"5","key":"2026101610062643000_cit109","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.1021\/acscatal.3c04461","article-title":"Discovery of Toxin-Degrading Enzymes with Positive Unlabeled Deep Learning","volume":"14","author":"Zhang","year":"2024","journal-title":"ACS Catal."},{"issue":"2","key":"2026101610062643000_cit110","doi-asserted-by":"publisher","first-page":"bbae089","DOI":"10.1093\/bib\/bbae089","article-title":"High-throughput prediction of enzyme promiscuity based on substrate-product pairs","volume":"25","author":"Xing","year":"2024","journal-title":"Briefings Bioinf."},{"issue":"9","key":"2026101610062643000_cit111","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1093\/bioinformatics\/bty813","article-title":"High precision protein functional site detection using 3D convolutional neural networks","volume":"35","author":"Torng","year":"2019","journal-title":"Bioinformatics"},{"issue":"6","key":"2026101610062643000_cit112","doi-asserted-by":"publisher","first-page":"1554","DOI":"10.1021\/jacsau.3c00188","article-title":"AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design","volume":"3","author":"Casadevall","year":"2023","journal-title":"JACS Au"},{"issue":"1","key":"2026101610062643000_cit113","doi-asserted-by":"publisher","first-page":"bbac599","DOI":"10.1093\/bib\/bbac599","article-title":"Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings","volume":"24","author":"Yeung","year":"2023","journal-title":"Briefings Bioinf."},{"issue":"5","key":"2026101610062643000_cit114","doi-asserted-by":"publisher","first-page":"bbae381","DOI":"10.1093\/bib\/bbae381","article-title":"Machine learning-assisted substrate binding pocket engineering based on structural information","volume":"25","author":"Wang","year":"2024","journal-title":"Briefings Bioinf."},{"issue":"1","key":"2026101610062643000_cit115","doi-asserted-by":"publisher","first-page":"7348","DOI":"10.1038\/s41467-024-51511-6","article-title":"Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites","volume":"15","author":"Wang","year":"2024","journal-title":"Nat. Commun."},{"issue":"5","key":"2026101610062643000_cit116","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1109\/tcbb.2024.3378470","article-title":"Transformer and Deep Forest Fusion Model for Predicting Coronavirus 3C-Like Protease Cleavage Sites","volume":"21","author":"Wang","year":"2024","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"2026101610062643000_cit117","doi-asserted-by":"publisher","first-page":"2024.10.14.618330","DOI":"10.1101\/2024.10.14.618330","article-title":"Assessing substrate scope of the cyclodehydratase LynD by mRNA display-enabled machine learning models","author":"Steude","year":"2024","journal-title":"bioRxiv"},{"issue":"2","key":"2026101610062643000_cit118","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s42256-024-00792-z","article-title":"State-specific protein-ligand complex structure prediction with a multiscale deep generative model","volume":"6","author":"Qiao","year":"2024","journal-title":"Nature Machine Intelligence"},{"issue":"2","key":"2026101610062643000_cit119","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/s00239-023-10100-9","article-title":"Structural Characterization of the Chlorophyllide a Oxygenase (CAO) Enzyme Through an In Silico Approach","volume":"91","author":"Dey","year":"2023","journal-title":"J. Mol. Evol."},{"issue":"8","key":"2026101610062643000_cit120","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1038\/s41929-022-00798-z","article-title":"Deep learning-based k cat prediction enables improved enzyme-constrained model reconstruction","volume":"5","author":"Li","year":"2022","journal-title":"Nat. Catal."},{"issue":"1","key":"2026101610062643000_cit121","doi-asserted-by":"publisher","first-page":"bpae061","DOI":"10.1093\/biomethods\/bpae061","article-title":"DLKcat cannot predict meaningful kcat values for mutants and unfamiliar enzymes","volume":"9","author":"Kroll","year":"2024","journal-title":"Biol. Methods Protoc."},{"issue":"5","key":"2026101610062643000_cit122","doi-asserted-by":"publisher","first-page":"bbae409","DOI":"10.1093\/bib\/bbae409","article-title":"DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures","volume":"25","author":"Wang","year":"2024","journal-title":"Briefings Bioinf."},{"issue":"9","key":"2026101610062643000_cit123","doi-asserted-by":"publisher","first-page":"101094","DOI":"10.1016\/j.checat.2024.101094","article-title":"EITLEM-Kinetics: A deep-learning framework for kinetic parameter prediction of mutant enzymes","volume":"4","author":"Shen","year":"2024","journal-title":"Chem Catal."},{"issue":"5","key":"2026101610062643000_cit124","doi-asserted-by":"publisher","first-page":"bbae387","DOI":"10.1093\/bib\/bbae387","article-title":"MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction","volume":"25","author":"Wang","year":"2024","journal-title":"Briefings Bioinf."},{"issue":"1","key":"2026101610062643000_cit125","doi-asserted-by":"publisher","first-page":"4139","DOI":"10.1038\/s41467-023-39840-4","article-title":"Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning","volume":"14","author":"Kroll","year":"2023","journal-title":"Nat. Commun."},{"issue":"1","key":"2026101610062643000_cit126","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1038\/s41467-025-57215-9","article-title":"CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters","volume":"16","author":"Boorla","year":"2025","journal-title":"Nat. Commun."},{"issue":"8","key":"2026101610062643000_cit127","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.1021\/acs.jcim.4c00058","article-title":"ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes","volume":"64","author":"Wang","year":"2024","journal-title":"J. Chem. Inf. Model."},{"key":"2026101610062643000_cit128","doi-asserted-by":"publisher","first-page":"arXiv.2305.11917","DOI":"10.48550\/arXiv.2305.11917","article-title":"Interpretable neural architecture search and transfer learning for understanding CRISPR\/Cas9 off-target enzymatic reactions","author":"Zhang","year":"2023","journal-title":"arXiv"},{"issue":"2","key":"2026101610062643000_cit129","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1109\/tcbb.2022.3190266","article-title":"Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples","volume":"20","author":"Nguyen","year":"2023","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"1","key":"2026101610062643000_cit130","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1186\/s12859-024-05746-1","article-title":"GraphKM: machine and deep learning for KM prediction of wildtype and mutant enzymes","volume":"25","author":"He","year":"2024","journal-title":"BMC Bioinf."},{"issue":"43","key":"2026101610062643000_cit131","doi-asserted-by":"publisher","first-page":"12073","DOI":"10.1039\/d3sc02752j","article-title":"EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution","volume":"14","author":"Ran","year":"2023","journal-title":"Chem. Sci."},{"issue":"5","key":"2026101610062643000_cit132","doi-asserted-by":"publisher","first-page":"0153","DOI":"10.34133\/research.0153","article-title":"Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework","volume":"2023","author":"Zhenkun","year":"2023","journal-title":"Research"},{"issue":"1","key":"2026101610062643000_cit133","doi-asserted-by":"publisher","first-page":"vbae103","DOI":"10.1093\/bioadv\/vbae103","article-title":"TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms","volume":"4","author":"Rodella","year":"2024","journal-title":"Bioinformatics Advances"},{"key":"2026101610062643000_cit134","doi-asserted-by":"publisher","first-page":"5544","DOI":"10.1016\/j.csbj.2023.11.006","article-title":"DeepTM: A deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences","volume":"21","author":"Li","year":"2023","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"3","key":"2026101610062643000_cit135","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.3390\/ijms24032217","article-title":"DeepTP: A Deep Learning Model for Thermophilic Protein Prediction","volume":"24","author":"Zhao","year":"2023","journal-title":"Int. J. Mol. Sci."},{"issue":"5","key":"2026101610062643000_cit136","doi-asserted-by":"publisher","first-page":"2858","DOI":"10.3390\/app13052858","article-title":"Identification of Thermophilic Proteins Based on Sequence-Based Bidirectional Representations from Transformer-Embedding Features","volume":"13","author":"Pei","year":"2023","journal-title":"Applied Sciences-Basel"},{"key":"2026101610062643000_cit137","doi-asserted-by":"publisher","first-page":"0031","DOI":"10.34133\/bdr.0031","article-title":"High-Temperature Tolerance Protein Engineering through Deep Evolution","volume":"6","author":"Chu","year":"2024","journal-title":"BioDesign Res."},{"issue":"11","key":"2026101610062643000_cit138","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2313809121","article-title":"Context- dependent design of induced- fit enzymes using deep learning generates well- expressed, thermally stable and active enzymes","volume":"121","author":"Zimmerman","year":"2024","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"2026101610062643000_cit139","doi-asserted-by":"publisher","first-page":"140468","DOI":"10.1016\/j.ijbiomac.2025.140468","article-title":"Improving the thermostability of ulvan lyase from polysaccharide lyase family 25 based on multiple computational rational design strategies","volume":"302","author":"Du","year":"2025","journal-title":"Int. J. Biol. Macromol."},{"key":"2026101610062643000_cit140","doi-asserted-by":"publisher","first-page":"2023.06.22.544776","DOI":"10.1101\/2023.06.22.544776","article-title":"Deep learning prediction of enzyme optimum pH","volume":"2023","author":"Gado","year":"2023","journal-title":"bioRxiv"},{"key":"2026101610062643000_cit141","doi-asserted-by":"publisher","first-page":"11.16.623957","DOI":"10.1101\/2024.11.16.623957","article-title":"Deep learning-based prediction of enzyme optimal pH and design of point mutations to improve acid resistance","author":"Qiu","year":"2024","journal-title":"bioRxiv"},{"key":"2026101610062643000_cit142","doi-asserted-by":"publisher","first-page":"2024.08.12.607600","DOI":"10.1101\/2024.08.12.607600","article-title":"Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature","author":"Qiu","year":"2024","journal-title":"bioRxiv"},{"key":"2026101610062643000_cit143","doi-asserted-by":"publisher","first-page":"3761","DOI":"10.1021\/acs.jcim.4c02291","article-title":"A Deep Retrieval-Enhanced Meta-Learning Framework for Enzyme Optimum pH Prediction","volume":"65","author":"Zhang","year":"2025","journal-title":"J. Chem. Inf. Model."},{"issue":"12","key":"2026101610062643000_cit144","doi-asserted-by":"publisher","first-page":"4670","DOI":"10.13345\/j.cjb.240255","article-title":"AcidBasePred: a protein acid-base tolerance prediction platform based on deep learning","volume":"40","author":"Huang","year":"2024","journal-title":"Sheng Wu Gong Cheng Xue Bao"},{"issue":"1","key":"2026101610062643000_cit145","doi-asserted-by":"publisher","first-page":"3274","DOI":"10.1038\/s41467-025-58521-y","article-title":"ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties","volume":"16","author":"Qian","year":"2025","journal-title":"Nat. Commun."},{"issue":"8056","key":"2026101610062643000_cit146","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1038\/s41586-024-08553-z","article-title":"A metagenomic \u2018dark matter\u2019enzyme catalyses oxidative cellulose conversion","volume":"639","author":"Santos","year":"2025","journal-title":"Nature"},{"issue":"1","key":"2026101610062643000_cit147","doi-asserted-by":"publisher","first-page":"lqae025","DOI":"10.1093\/nargab\/lqae025","article-title":"Mining metagenomics data for novel bacterial nanocompartments","volume":"6","author":"Kashif-Khan","year":"2024","journal-title":"NAR:Genomics Bioinf."},{"issue":"1","key":"2026101610062643000_cit148","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s13659-023-00426-8","article-title":"Precision enzyme discovery through targeted mining of metagenomic data","volume":"14","author":"Ariaeenejad","year":"2024","journal-title":"Nat. Prod. Bioprospect."},{"key":"2026101610062643000_cit149","doi-asserted-by":"publisher","first-page":"117640","DOI":"10.1016\/j.ecoenv.2024.117640","article-title":"Metagenomic exploration and computational prediction of novel enzymes for polyethylene terephthalate degradation","volume":"289","author":"Jahanshahi","year":"2025","journal-title":"Ecotoxicol. Environ. Saf."},{"issue":"1","key":"2026101610062643000_cit150","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s00253-023-12926-1","article-title":"Mining and rational design of psychrophilic catalases using metagenomics and deep learning models","volume":"108","author":"Wu","year":"2024","journal-title":"Appl. Microbiol. Biotechnol."}],"container-title":["ACS Synthetic Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acssynbio.6c00633","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/asbcd6\/article-pdf\/15\/10\/3937\/68385790\/sb6c00633.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/asbcd6\/article-pdf\/15\/10\/3937\/68385790\/sb6c00633.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T14:24:22Z","timestamp":1792160662000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/asbcd6\/article\/15\/10\/3937\/5421291\/Deep-Learning-in-Enzyme-Function-Prediction-and"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,4]]},"references-count":150,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,9,4]]},"published-print":{"date-parts":[[2026,10,16]]}},"URL":"https:\/\/doi.org\/10.1021\/acssynbio.6c00633","relation":{},"ISSN":["2161-5063"],"issn-type":[{"value":"2161-5063","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,4]]}}}