{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:58:31Z","timestamp":1784905111729,"version":"3.55.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:00:00Z","timestamp":1770076800000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Nos. 62322112"],"award-info":[{"award-number":["Nos. 62322112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Internal Research Grants of Macao Polytechnic University","award":["no. RP\/CAI-02\/2023"],"award-info":[{"award-number":["no. RP\/CAI-02\/2023"]}]},{"DOI":"10.13039\/501100003009","name":"the Science and Technology Development Fund","doi-asserted-by":"crossref","award":["no. 0177\/2023\/RIA3"],"award-info":[{"award-number":["no. 0177\/2023\/RIA3"]}],"id":[{"id":"10.13039\/501100003009","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"DOI":"10.1186\/s13321-025-01144-8","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T16:30:46Z","timestamp":1767630646000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction"],"prefix":"10.1186","volume":"18","author":[{"given":"Changhang","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuwen","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinjin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zilong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leyi","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"issue":"12","key":"1144_CR1","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1038\/nbt1267","volume":"24","author":"RE Hancock","year":"2006","unstructured":"Hancock RE, Sahl H-G (2006) Antimicrobial and host-defense peptides as new anti-infective therapeutic strategies. Nat Biotechnol 24(12):1551\u20131557","journal-title":"Nat Biotechnol"},{"issue":"3","key":"1144_CR2","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1038\/nrmicro1098","volume":"3","author":"KA Brogden","year":"2005","unstructured":"Brogden KA (2005) Antimicrobial peptides: pore formers or metabolic inhibitors in bacteria? Nat Rev Microbiol 3(3):238\u2013250","journal-title":"Nat Rev Microbiol"},{"issue":"6657","key":"1144_CR3","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1126\/science.abq3178","volume":"381","author":"JF Pierre","year":"2023","unstructured":"Pierre JF et al (2023) Peptide YY: a Paneth cell antimicrobial peptide that maintains Candida gut commensalism. Science 381(6657):502\u2013508","journal-title":"Science"},{"issue":"6054","key":"1144_CR4","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1126\/science.1209728","volume":"334","author":"FH Login","year":"2011","unstructured":"Login FH et al (2011) Antimicrobial peptides keep insect endosymbionts under control. Science 334(6054):362\u2013365","journal-title":"Science"},{"issue":"13","key":"1144_CR5","doi-asserted-by":"publisher","first-page":"7820","DOI":"10.1039\/D0CS00729C","volume":"50","author":"BH Gan","year":"2021","unstructured":"Gan BH et al (2021) The multifaceted nature of antimicrobial peptides: current synthetic chemistry approaches and future directions. Chem Soc Rev 50(13):7820\u20137880","journal-title":"Chem Soc Rev"},{"key":"1144_CR6","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5c00530","author":"C Lin","year":"2025","unstructured":"Lin C et al (2025) Deep learning in antimicrobial peptide prediction. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.5c00530","journal-title":"J Chem Inf Model"},{"key":"1144_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-024-4147-8","author":"K Yan","year":"2024","unstructured":"Yan K et al (2024) TPpred-SC: multi-functional therapeutic peptide prediction based on multi-label supervised contrastive learning. Sci China Inf Sci. https:\/\/doi.org\/10.1007\/s11432-024-4147-8","journal-title":"Sci China Inf Sci"},{"issue":"7","key":"1144_CR8","doi-asserted-by":"publisher","first-page":"2931","DOI":"10.1021\/acs.jproteome.9b00250","volume":"18","author":"X Ru","year":"2019","unstructured":"Ru X, Li L, Zou Q (2019) Incorporating distance-based Top-n-gram and random forest to identify electron transport proteins. J Proteome Res 18(7):2931\u20132939","journal-title":"J Proteome Res"},{"issue":"14","key":"1144_CR9","doi-asserted-by":"publisher","first-page":"2058","DOI":"10.1093\/bioinformatics\/btaa917","volume":"37","author":"TJ Lawrence","year":"2021","unstructured":"Lawrence TJ et al (2021) AmPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool. Bioinformatics 37(14):2058\u20132060","journal-title":"Bioinformatics"},{"issue":"3","key":"1144_CR10","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1093\/bib\/bbz043","volume":"21","author":"C-R Chung","year":"2020","unstructured":"Chung C-R et al (2020) Characterization and identification of antimicrobial peptides with different functional activities. Brief Bioinform 21(3):1098\u20131114","journal-title":"Brief Bioinform"},{"issue":"1","key":"1144_CR11","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1038\/s41598-018-19752-w","volume":"8","author":"P Bhadra","year":"2018","unstructured":"Bhadra P et al (2018) AmPEP: sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Sci Rep 8(1):1697","journal-title":"Sci Rep"},{"issue":"11","key":"1144_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-024-4171-9","volume":"67","author":"Y Wang","year":"2024","unstructured":"Wang Y et al (2024) SBSM-Pro: support bio-sequence machine for proteins. Sci China Inf Sci 67(11):212106","journal-title":"Sci China Inf Sci"},{"issue":"1","key":"1144_CR13","doi-asserted-by":"publisher","first-page":"91","DOI":"10.2174\/1574893618666230417104543","volume":"19","author":"PK Meher","year":"2024","unstructured":"Meher PK et al (2024) SVM-Root: identification of root-associated proteins in plants by employing the support vector machine with sequence-derived features. Curr Bioinform 19(1):91\u2013102","journal-title":"Curr Bioinform"},{"issue":"21","key":"1144_CR14","doi-asserted-by":"publisher","first-page":"5262","DOI":"10.1093\/bioinformatics\/btaa653","volume":"36","author":"LC Fingerhut","year":"2020","unstructured":"Fingerhut LC et al (2020) ampir: an R package for fast genome-wide prediction of antimicrobial peptides. Bioinformatics 36(21):5262\u20135263","journal-title":"Bioinformatics"},{"issue":"5","key":"1144_CR15","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1109\/TCBB.2012.89","volume":"9","author":"S Joseph","year":"2012","unstructured":"Joseph S et al (2012) ClassAMP: a prediction tool for classification of antimicrobial peptides. IEEE ACM Trans Comput Biol Bioinform 9(5):1535\u20131538","journal-title":"IEEE ACM Trans Comput Biol Bioinform"},{"key":"1144_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105577","volume":"146","author":"H Lv","year":"2022","unstructured":"Lv H et al (2022) AMPpred-EL: an effective antimicrobial peptide prediction model based on ensemble learning. Comput Biol Med 146:105577","journal-title":"Comput Biol Med"},{"issue":"21","key":"1144_CR17","doi-asserted-by":"publisher","first-page":"4272","DOI":"10.1093\/bioinformatics\/btz246","volume":"35","author":"L Wei","year":"2019","unstructured":"Wei L et al (2019) PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning. Bioinformatics 35(21):4272\u20134280","journal-title":"Bioinformatics"},{"key":"1144_CR18","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1291352","author":"H Zulfiqar","year":"2024","unstructured":"Zulfiqar H et al (2024) Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings. Front Med. https:\/\/doi.org\/10.3389\/fmed.2023.1291352","journal-title":"Front Med"},{"key":"1144_CR19","doi-asserted-by":"publisher","DOI":"10.2174\/0113894501322734241008163304","author":"M Mahapatra","year":"2024","unstructured":"Mahapatra M, Sahu C, Mohapatra S (2024) Trends of artificial intelligence (AI) use in drug targets, discovery and development: current status and future perspectives. Curr Drug Targets. https:\/\/doi.org\/10.2174\/0113894501322734241008163304","journal-title":"Curr Drug Targets"},{"key":"1144_CR20","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad217","author":"Q Meng","year":"2023","unstructured":"Meng Q, Guo F, Tang J (2023) Improved structure-related prediction for insufficient homologous proteins using MSA enhancement and pre-trained language model. Brief Bioinform. https:\/\/doi.org\/10.1093\/bib\/bbad217","journal-title":"Brief Bioinform"},{"issue":"22","key":"1144_CR21","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkab829","volume":"49","author":"H Li","year":"2021","unstructured":"Li H, Pang Y, Liu B (2021) BioSeq-BLM: a platform for analyzing DNA, RNA, and protein sequences based on biological language models. Nucleic Acids Res 49(22):e129","journal-title":"Nucleic Acids Res"},{"issue":"20","key":"1144_CR22","doi-asserted-by":"publisher","first-page":"e127","DOI":"10.1093\/nar\/gkz740","volume":"47","author":"B Liu","year":"2019","unstructured":"Liu B, Gao X, Zhang H (2019) BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches. Nucleic Acids Res 47(20):e127","journal-title":"Nucleic Acids Res"},{"key":"1144_CR23","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1016\/j.omtn.2020.05.006","volume":"20","author":"J Yan","year":"2020","unstructured":"Yan J et al (2020) Deep-AmPEP30: improve short antimicrobial peptides prediction with deep learning. Mol Ther Nucleic Acids 20:882\u2013894","journal-title":"Mol Ther Nucleic Acids"},{"issue":"5","key":"1144_CR24","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae305","volume":"40","author":"M Ullah","year":"2024","unstructured":"Ullah M et al (2024) DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm. Bioinformatics 40(5):btae305","journal-title":"Bioinformatics"},{"issue":"12","key":"1144_CR25","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1093\/bioinformatics\/bty937","volume":"35","author":"M-N Hamid","year":"2019","unstructured":"Hamid M-N, Friedberg I (2019) Identifying antimicrobial peptides using word embedding with deep recurrent neural networks. Bioinformatics 35(12):2009\u20132016","journal-title":"Bioinformatics"},{"issue":"17","key":"1144_CR26","doi-asserted-by":"publisher","DOI":"10.1002\/cmdc.202200291","volume":"17","author":"E Zakharova","year":"2022","unstructured":"Zakharova E et al (2022) Machine learning guided discovery of non-hemolytic membrane disruptive anticancer peptides. ChemMedChem 17(17):e202200291","journal-title":"ChemMedChem"},{"key":"1144_CR27","doi-asserted-by":"publisher","first-page":"1232117","DOI":"10.3389\/fgene.2023.1232117","volume":"14","author":"Y Wang","year":"2023","unstructured":"Wang Y et al (2023) AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides. Front Genet 14:1232117","journal-title":"Front Genet"},{"issue":"16","key":"1144_CR28","doi-asserted-by":"publisher","first-page":"2740","DOI":"10.1093\/bioinformatics\/bty179","volume":"34","author":"D Veltri","year":"2018","unstructured":"Veltri D, Kamath U, Shehu A (2018) Deep learning improves antimicrobial peptide recognition. Bioinformatics 34(16):2740\u20132747","journal-title":"Bioinformatics"},{"key":"1144_CR29","first-page":"1","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A et al (2017) Attention is all you need. Adv Neural Inf Process Syst 30:1","journal-title":"Adv Neural Inf Process Syst"},{"issue":"6","key":"1144_CR30","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab200","volume":"22","author":"Y Zhang","year":"2021","unstructured":"Zhang Y et al (2021) A novel antibacterial peptide recognition algorithm based on BERT. Brief Bioinform 22(6):bbab200","journal-title":"Brief Bioinform"},{"issue":"6637","key":"1144_CR31","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","volume":"379","author":"Z Lin","year":"2023","unstructured":"Lin Z et al (2023) Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637):1123\u20131130","journal-title":"Science"},{"key":"1144_CR32","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2025.1529335","author":"H-Q Zhang","year":"2025","unstructured":"Zhang H-Q et al (2025) PMPred-AE: a computational model for the detection and interpretation of pathological myopia based on artificial intelligence. Front Med. https:\/\/doi.org\/10.3389\/fmed.2025.1529335","journal-title":"Front Med"},{"key":"1144_CR33","doi-asserted-by":"publisher","first-page":"1062576","DOI":"10.3389\/fgene.2022.1062576","volume":"13","author":"T-J Sun","year":"2022","unstructured":"Sun T-J et al (2022) LABAMPsGCN: a framework for identifying lactic acid bacteria antimicrobial peptides based on graph convolutional neural network. Front Genet 13:1062576","journal-title":"Front Genet"},{"issue":"1","key":"1144_CR34","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac715","volume":"39","author":"K Yan","year":"2023","unstructured":"Yan K et al (2023) sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure. Bioinformatics 39(1):btac715","journal-title":"Bioinformatics"},{"issue":"4","key":"1144_CR35","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae308","volume":"25","author":"N Chen","year":"2024","unstructured":"Chen N et al (2024) TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation. Brief Bioinform 25(4):bbae308","journal-title":"Brief Bioinform"},{"issue":"10","key":"1144_CR36","doi-asserted-by":"publisher","first-page":"4310","DOI":"10.1021\/acs.jcim.3c02061","volume":"64","author":"G Cordoves-Delgado","year":"2024","unstructured":"Cordoves-Delgado G, Garc\u00eda-Jacas CR (2024) Predicting antimicrobial peptides using ESMFold-predicted structures and ESM-2-based amino acid features with graph deep learning. J Chem Inf Model 64(10):4310\u20134321","journal-title":"J Chem Inf Model"},{"key":"1144_CR37","unstructured":"Schilling V, Dubey A, Hattab G. PepTriX: a framework for explainable peptide analysis through protein language models. arXiv preprint arXiv:2511.10244. 2025."},{"key":"1144_CR38","unstructured":"Ramos J. Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning. Citeseer. 2003. 242: 29\u201348."},{"key":"1144_CR39","doi-asserted-by":"crossref","unstructured":"Lin Y., et al. Bertgcn: transductive text classification by combining gcn and bert. arXiv preprint arXiv:2105.05727. 2021.","DOI":"10.18653\/v1\/2021.findings-acl.126"},{"key":"1144_CR40","unstructured":"Veli\u010dkovi\u0107 P., et al. Graph attention networks. arXiv preprint arXiv:1710.10903. 2017."},{"key":"1144_CR41","unstructured":"Wang M., et al., Deep graph library: a graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315. 2019."},{"issue":"5","key":"1144_CR42","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab083","volume":"22","author":"J Xu","year":"2021","unstructured":"Xu J et al (2021) Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides. Brief Bioinform 22(5):bbab083","journal-title":"Brief Bioinform"},{"issue":"D1","key":"1144_CR43","doi-asserted-by":"publisher","first-page":"D1119","DOI":"10.1093\/nar\/gkv1114","volume":"44","author":"S Singh","year":"2016","unstructured":"Singh S et al (2016) SATPdb: a database of structurally annotated therapeutic peptides. Nucleic Acids Res 44(D1):D1119\u2013D1126","journal-title":"Nucleic Acids Res"},{"key":"1144_CR44","volume":"2015","author":"HT Lee","year":"2015","unstructured":"Lee HT et al (2015) A large-scale structural classification of antimicrobial peptides. Biomed Res Int 2015:475062","journal-title":"Biomed Res Int"},{"issue":"D1","key":"1144_CR45","doi-asserted-by":"publisher","first-page":"D1087","DOI":"10.1093\/nar\/gkv1278","volume":"44","author":"G Wang","year":"2016","unstructured":"Wang G, Li X, Wang Z (2016) APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res 44(D1):D1087\u2013D1093","journal-title":"Nucleic Acids Res"},{"key":"1144_CR46","doi-asserted-by":"publisher","first-page":"D774","DOI":"10.1093\/nar\/gkp1021","volume":"38","author":"S Thomas","year":"2010","unstructured":"Thomas S et al (2010) CAMP: a useful resource for research on antimicrobial peptides. Nucl Acid Res 38:D774-80","journal-title":"Nucl Acid Res"},{"issue":"D1","key":"1144_CR47","doi-asserted-by":"publisher","first-page":"D1094","DOI":"10.1093\/nar\/gkv1051","volume":"44","author":"FH Waghu","year":"2016","unstructured":"Waghu FH et al (2016) CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides. Nucl Acid Res 44(D1):D1094\u2013D1097","journal-title":"Nucl Acid Res"},{"key":"1144_CR48","doi-asserted-by":"publisher","DOI":"10.1093\/database\/baaa061","author":"G Ye","year":"2020","unstructured":"Ye G et al (2020) LAMP2: a major update of the database linking antimicrobial peptides. Database (Oxford). https:\/\/doi.org\/10.1093\/database\/baaa061","journal-title":"Database (Oxford)"},{"issue":"6","key":"1144_CR49","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0066557","volume":"8","author":"X Zhao","year":"2013","unstructured":"Zhao X et al (2013) LAMP: a database linking antimicrobial peptides. PLoS ONE 8(6):e66557","journal-title":"PLoS ONE"},{"key":"1144_CR50","doi-asserted-by":"publisher","first-page":"24482","DOI":"10.1038\/srep24482","volume":"6","author":"L Fan","year":"2016","unstructured":"Fan L et al (2016) DRAMP: a comprehensive data repository of antimicrobial peptides. Sci Rep 6:24482","journal-title":"Sci Rep"},{"issue":"D1","key":"1144_CR51","doi-asserted-by":"publisher","first-page":"D285","DOI":"10.1093\/nar\/gky1030","volume":"47","author":"JH Jhong","year":"2019","unstructured":"Jhong JH et al (2019) dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data. Nucleic Acids Res 47(D1):D285\u2013D297","journal-title":"Nucleic Acids Res"},{"issue":"5","key":"1144_CR52","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1093\/bioinformatics\/btq003","volume":"26","author":"Y Huang","year":"2010","unstructured":"Huang Y et al (2010) CD-HIT suite: a web server for clustering and comparing biological sequences. Bioinformatics 26(5):680\u2013682","journal-title":"Bioinformatics"},{"issue":"D1","key":"1144_CR53","doi-asserted-by":"publisher","first-page":"D204","DOI":"10.1093\/nar\/gku989","volume":"43","author":"C UniProt","year":"2015","unstructured":"UniProt C (2015) UniProt: a hub for protein information. Nucleic Acids Res 43(D1):D204\u2013D212","journal-title":"Nucleic Acids Res"},{"key":"1144_CR54","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1281880","volume":"10","author":"X Zou","year":"2023","unstructured":"Zou X et al (2023) Accurately identifying hemagglutinin using sequence information and machine learning methods. Front Med (Lausanne) 10:1281880","journal-title":"Front Med (Lausanne)"},{"issue":"15","key":"1144_CR55","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.2174\/0113894501330963240905083020","volume":"25","author":"GA Abdelkader","year":"2024","unstructured":"Abdelkader GA, Kim JD (2024) Advances in protein-ligand binding affinity prediction via deep learning: a comprehensive study of datasets, data preprocessing techniques, and model architectures. Curr Drug Targets 25(15):1041\u20131065","journal-title":"Curr Drug Targets"},{"issue":"1","key":"1144_CR56","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1186\/s12915-024-01968-0","volume":"22","author":"H Zhu","year":"2024","unstructured":"Zhu H, Hao H, Yu L (2024) Identification of microbe\u2013disease signed associations via multi-scale variational graph autoencoder based on signed message propagation. BMC Biol 22(1):172","journal-title":"BMC Biol"},{"issue":"1","key":"1144_CR57","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1186\/s12915-024-02085-8","volume":"22","author":"Z Huang","year":"2024","unstructured":"Huang Z et al (2024) Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data. BMC Biol 22(1):290","journal-title":"BMC Biol"},{"issue":"7","key":"1144_CR58","doi-asserted-by":"publisher","first-page":"2306329","DOI":"10.1002\/advs.202306329","volume":"11","author":"X Guo","year":"2024","unstructured":"Guo X et al (2024) Highly accurate estimation of cell type abundance in bulk tissues based on single-cell reference and domain adaptive matching. Adv Sci 11(7):2306329","journal-title":"Adv Sci"},{"issue":"1","key":"1144_CR59","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1186\/s12864-022-08310-4","volume":"23","author":"C Li","year":"2022","unstructured":"Li C et al (2022) AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens. BMC Genomics 23(1):77","journal-title":"BMC Genomics"},{"issue":"7","key":"1144_CR60","doi-asserted-by":"publisher","first-page":"2393","DOI":"10.1021\/acs.jcim.3c01017","volume":"64","author":"C Li","year":"2023","unstructured":"Li C et al (2023) AMPpred-MFA: an interpretable antimicrobial peptide predictor with a stacking architecture, multiple features, and multihead attention. J Chem Inf Model 64(7):2393\u20132404","journal-title":"J Chem Inf Model"},{"key":"1144_CR61","unstructured":"Devlin, J. et al. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 2019."},{"key":"1144_CR62","unstructured":"Duan, L. et al. Epileptic seizure prediction based on convolutional recurrent neural network with multi-timescale. In intelligence science and big data engineering. Big data and machine learning: 9th international conference, IScIDE 2019, Nanjing, China, October 17\u201320, 2019, Proceedings, Part II 9. Springer: Cham. 2019."},{"key":"1144_CR63","doi-asserted-by":"crossref","unstructured":"Lee H, Lee J, Kim TY. SUMBT: slot-utterance matching for universal and scalable belief tracking. arXiv preprint arXiv:1907.07421. 2019.","DOI":"10.18653\/v1\/P19-1546"},{"key":"1144_CR64","unstructured":"Xu, K., et al. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826. 2018."},{"key":"1144_CR65","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1706.02216","author":"W Hamilton","year":"2017","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neur Inform Process Syst. https:\/\/doi.org\/10.48550\/arXiv.1706.02216","journal-title":"Adv Neur Inform Process Syst"},{"key":"1144_CR66","first-page":"3844","volume":"29","author":"M Defferrard","year":"2016","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29:3844\u20133852","journal-title":"Adv Neural Inf Process Syst"},{"issue":"6","key":"1144_CR67","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab258","volume":"22","author":"PB Timmons","year":"2021","unstructured":"Timmons PB, Hewage CM (2021) ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides. Brief Bioinform 22(6):bbab258","journal-title":"Brief Bioinform"},{"issue":"6","key":"1144_CR68","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad353","volume":"24","author":"R Cao","year":"2023","unstructured":"Cao R et al (2023) FFMAVP: a new classifier based on feature fusion and multitask learning for identifying antiviral peptides and their subclasses. Brief Bioinform 24(6):bbad353","journal-title":"Brief Bioinform"},{"issue":"6","key":"1144_CR69","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab263","volume":"22","author":"Y Pang","year":"2021","unstructured":"Pang Y et al (2021) AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches. Brief Bioinform 22(6):bbab263","journal-title":"Brief Bioinform"},{"issue":"8","key":"1144_CR70","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.3390\/ijms20081964","volume":"20","author":"V Boopathi","year":"2019","unstructured":"Boopathi V et al (2019) Macppred: a support vector machine-based meta-predictor for identification of anticancer peptides. Int J Mol Sci 20(8):1964","journal-title":"Int J Mol Sci"},{"key":"1144_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108063","volume":"170","author":"J Bian","year":"2024","unstructured":"Bian J et al (2024) ACP-ML: a sequence-based method for anticancer peptide prediction. Comput Biol Med 170:108063","journal-title":"Comput Biol Med"},{"issue":"1","key":"1144_CR72","doi-asserted-by":"publisher","first-page":"13594","DOI":"10.1038\/s41598-021-93124-9","volume":"11","author":"K-Y Huang","year":"2021","unstructured":"Huang K-Y et al (2021) Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties. Sci Rep 11(1):13594","journal-title":"Sci Rep"},{"issue":"10","key":"1144_CR73","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.3390\/molecules24101973","volume":"24","author":"N Schaduangrat","year":"2019","unstructured":"Schaduangrat N et al (2019) ACpred: a computational tool for the prediction and analysis of anticancer peptides. Molecules 24(10):1973","journal-title":"Molecules"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-01144-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01144-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01144-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T14:04:09Z","timestamp":1770127449000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13321-025-01144-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":73,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1144"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-01144-8","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,5]]},"assertion":[{"value":"12 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"15"}}