{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:11:42Z","timestamp":1783815102027,"version":"3.55.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2023NSFSC0128"],"award-info":[{"award-number":["2023NSFSC0128"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["ZYGX2021YGLH209"],"award-info":[{"award-number":["ZYGX2021YGLH209"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Aided Mol Des"],"DOI":"10.1007\/s10822-026-00869-4","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:31:47Z","timestamp":1782408707000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Quantitative structure\u2013activity relationship characterization and modeling of length-varying bioactive peptides"],"prefix":"10.1007","volume":"40","author":[{"given":"Yunyi","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5681-9937","authenticated-orcid":false,"given":"Peng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,25]]},"reference":[{"key":"869_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s43586-023-00205-2","volume":"3","author":"R Hellinger","year":"2023","unstructured":"Hellinger R, Sigurdsson A, Wu W, Romanova EV, Li L, Sweedler JV, S\u00fcssmuth RD, Gruber CW (2023) Peptidomics Nat Rev Methods Primers 3:25","journal-title":"Peptidomics Nat Rev Methods Primers"},{"key":"869_CR2","volume":"9","author":"Z Du","year":"2022","unstructured":"Du Z, Li Y (2022) Review and perspective on bioactive peptides: A roadmap for research, development, and future opportunities. J Agr Food Res 9:100353","journal-title":"J Agr Food Res"},{"key":"869_CR3","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1039\/C9MO00041K","volume":"15","author":"P Zhou","year":"2019","unstructured":"Zhou P, Miao Q, Yan F, Li Z, Jiang Q, Wen L, Meng Y (2019) Is protein context responsible for peptide-mediated interactions? Mol Omics 15:280\u2013295","journal-title":"Mol Omics"},{"key":"869_CR4","doi-asserted-by":"publisher","first-page":"6733","DOI":"10.1021\/cr400585q","volume":"114","author":"K Van Roey","year":"2014","unstructured":"Van Roey K, Uyar B, Weatheritt RJ, Dinkel H, Seiler M, Budd A, Gibson TJ, Davey NE (2014) Short linear motifs: ubiquitous and functionally diverse protein interaction modules directing cell regulation. Chem Rev 114:6733\u20136778","journal-title":"Chem Rev"},{"key":"869_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/pmic.202200175","volume":"23","author":"J Lin","year":"2023","unstructured":"Lin J, Wang S, Wen L, Ye H, Shang S, Li J, Shu J, Zhou P (2023) Targeting peptide-mediated interactions in omics. Proteomics 23:e2200175","journal-title":"Proteomics"},{"key":"869_CR6","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1021\/ci500522v","volume":"55","author":"C Yang","year":"2015","unstructured":"Yang C, Zhang S, He P, Wang C, Huang J, Zhou P (2015) Self-binding peptides: folding or binding? J Chem Inf Model 55:329\u2013342","journal-title":"J Chem Inf Model"},{"key":"869_CR7","doi-asserted-by":"publisher","first-page":"652","DOI":"10.2174\/1381612829666230201121353","volume":"29","author":"Q Wen","year":"2023","unstructured":"Wen Q, Zhang L, Zhao F, Chen Y, Su Y, Zhang X, Chen P, Zheng T (2023) Production technology and functionality of bioactive peptides. Curr Pharm Des 29:652\u2013674","journal-title":"Curr Pharm Des"},{"key":"869_CR8","doi-asserted-by":"publisher","first-page":"9609","DOI":"10.1021\/acs.jcim.4c01758","volume":"64","author":"S Akbar","year":"2024","unstructured":"Akbar S, Ullah M, Raza A, Zou Q, Alghamdi W (2024) DeepAIPs-Pred: predicting anti-inflammatory peptides using local evolutionary transformation images and structural embedding-based qptimal descriptors with self-normalized BiTCNs. J Chem Inf Model 64:9609\u20139625","journal-title":"J Chem Inf Model"},{"key":"869_CR9","doi-asserted-by":"publisher","first-page":"35747","DOI":"10.1021\/acsomega.5c02422","volume":"10","author":"S Akbar","year":"2025","unstructured":"Akbar S, Raza A, Alghamdi W, Saeed A, Ali H, Zou Q (2025) DeepAIPs-SFLA: deep convolutional model for prediction of anti-inflammatory peptides using binary pattern decomposition of novel multiview descriptors with an SFLA approach. ACS Omega 10:35747\u201335762","journal-title":"ACS Omega"},{"key":"869_CR10","doi-asserted-by":"publisher","first-page":"9815","DOI":"10.1021\/acs.jcim.5c01777","volume":"65","author":"S Akbar","year":"2025","unstructured":"Akbar S, Raza A, Zou Q, Alghamdi W, Kang X, Ali H, Luo X (2025) Accelerating prediction of antiviral peptides using genetic algorithm-based weighted multiperspective descriptors with self-normalized deep networks. J Chem Inf Model 65:9815\u20139830","journal-title":"J Chem Inf Model"},{"key":"869_CR11","doi-asserted-by":"publisher","first-page":"12403","DOI":"10.1021\/acsomega.4c11449","volume":"10","author":"S Akbar","year":"2025","unstructured":"Akbar S, Raza A, Awan HH, Zou Q, Alghamdi W, Saeed A (2025) pNPs-CapsNet: predicting neuropeptides using protein language models and fasttext encoding-based weighted multi-view feature integration with deep capsule neural network. ACS Omega 10:12403\u201312416","journal-title":"ACS Omega"},{"key":"869_CR12","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.1021\/jm00390a003","volume":"30","author":"S Hellberg","year":"1987","unstructured":"Hellberg S, Sj\u00f6str\u00f6m M, Skagerberg B, Wold S (1987) Peptide quantitative structure-activity relationships, a multivariate approach. J Med Chem 30:1126\u20131135","journal-title":"J Med Chem"},{"key":"869_CR13","doi-asserted-by":"publisher","first-page":"311","DOI":"10.2174\/157340908786785994","volume":"4","author":"P Zhou","year":"2008","unstructured":"Zhou P, Tian F, Wu Y, Li Z, Shang Z (2008) Quantitative sequence-activity model (QSAM): applying QSAR strategy to model and predict bioactivity and function of peptides, proteins and nucleic acids. Curr Comput Aided Drug Des 4:311\u2013321","journal-title":"Curr Comput Aided Drug Des"},{"key":"869_CR14","doi-asserted-by":"publisher","first-page":"1718","DOI":"10.1021\/acs.jcim.0c01370","volume":"61","author":"P Zhou","year":"2021","unstructured":"Zhou P, Liu Q, Wu T, Miao Q, Shang S, Wang H, Chen Z, Wang S, Wang H (2021) Systematic comparison and comprehensive evaluation of 80 amino acid descriptors in peptide QSAR modeling. J Chem Inf Model 61:1718\u20131731","journal-title":"J Chem Inf Model"},{"key":"869_CR15","doi-asserted-by":"publisher","first-page":"1036862","DOI":"10.3389\/fgene.2022.1036862","volume":"13","author":"J Zhao","year":"2022","unstructured":"Zhao J, Jiang H, Zou G, Lin Q, Wang Q, Liu J, Ma L (2022) CNNArginineMe: a CNN structure for training models for predicting arginine methylation sites based on the one-hot encoding of peptide sequence. Front Genet 13:1036862","journal-title":"Front Genet"},{"key":"869_CR16","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s10822-025-00710-4","volume":"40","author":"S Sadhukhan","year":"2025","unstructured":"Sadhukhan S, Bhattacharya R, Bhattcharya D, Sahana S, Pradhan B, Pandit S, Gill HS, Rajeev M, Nag M, Lahiri D (2025) Artificial intelligence in protein-based detection and inhibition of AMR pathways. J Comput Aided Mol Des 40:4","journal-title":"J Comput Aided Mol Des"},{"key":"869_CR17","doi-asserted-by":"publisher","first-page":"114137","DOI":"10.1016\/j.isci.2025.114137","volume":"28","author":"S Akbar","year":"2025","unstructured":"Akbar S, Raza A, Alghamdi W, Ali H, Zou Q, Luo X (2025) Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network. iScience 28:114137","journal-title":"iScience"},{"key":"869_CR18","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/0003-2670(93)80437-P","volume":"277","author":"S Wold","year":"1993","unstructured":"Wold S, Jonsson J, Sj\u00f6rstr\u00f6m M, Sandberg M, R\u00e4nnar S (1993) DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Anal Chim Acta 277:239\u2013253","journal-title":"Anal Chim Acta"},{"key":"869_CR19","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/S0169-7439(98)00062-8","volume":"42","author":"PM Andersson","year":"1998","unstructured":"Andersson PM, Sj\u00f6str\u00f6m M, Lundstedt T (1998) Preprocessing peptide sequences for multivariate sequence-property analysis. Chemometr Intel Lab Syst 42:41\u201350","journal-title":"Chemometr Intel Lab Syst"},{"key":"869_CR20","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1007\/s00726-019-02761-y","volume":"51","author":"M Bahador","year":"2019","unstructured":"Bahador M, Hemmateenejad B, Yousefinejad S (2019) Quantitative sequence\u2013activity modeling of ACE peptide originated from milk using ACC-QTMS amino acid indices. Amino Acids 51:1209\u20131220","journal-title":"Amino Acids"},{"key":"869_CR21","doi-asserted-by":"publisher","first-page":"S4","DOI":"10.1186\/1471-2105-14-S6-S4","volume":"14","author":"I Dimitrov","year":"2013","unstructured":"Dimitrov I, Flower DR, Doytchinova I (2013) AllerTOP\u2014a server for in silico prediction of allergens. BMC Bioinfor 14:S4","journal-title":"BMC Bioinfor"},{"key":"869_CR22","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1080\/1062936X.2025.2552141","volume":"36","author":"Y Zhang","year":"2025","unstructured":"Zhang Y, Li K, Gan Y, Zhou P (2025) Structural characterization of length-varying peptide sequences for peptide quantitative structure-activity relationship. SAR QSAR Environ Res 36:727\u2013751","journal-title":"SAR QSAR Environ Res"},{"key":"869_CR23","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.theochem.2004.04.010","volume":"679","author":"L Sun","year":"2004","unstructured":"Sun L, Zhou Y, Li G, Li SZ (2004) Molecular electronegativity-distance vector (MEDV-4): a two-dimensional QSAR method for the estimation and prediction of biological activities of estradiol derivatives. J Mol Struct Theochem 679:107\u2013113","journal-title":"J Mol Struct Theochem"},{"key":"869_CR24","doi-asserted-by":"publisher","first-page":"2705","DOI":"10.1021\/jm00014a022","volume":"38","author":"ER Collantes","year":"1995","unstructured":"Collantes ER, Dunn WJ (1995) Amino acid side chain descriptors for quantitative structure-activity relationship studies of peptide analogues. J Med Chem 38:2705\u20132713","journal-title":"J Med Chem"},{"key":"869_CR25","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s00726-022-03219-4","volume":"55","author":"J Lin","year":"2023","unstructured":"Lin J, Wen L, Zhou Y, Zhou P (2023) PepQSAR: a comprehensive data source and information platform for peptide quantitative structure-activity relationships. Amino Acids 55:235\u2013242","journal-title":"Amino Acids"},{"key":"869_CR26","doi-asserted-by":"publisher","first-page":"170","DOI":"10.2174\/1389200219666181012151944","volume":"20","author":"Z Li","year":"2019","unstructured":"Li Z, Miao Q, Yan F, Meng Y, Zhou P (2019) Machine learning in quantitative protein\u2013peptide affinity prediction: implications for therapeutic peptide design. Curr Drug Metab 20:170\u2013176","journal-title":"Curr Drug Metab"},{"key":"869_CR27","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","volume":"58","author":"S Wold","year":"2001","unstructured":"Wold S, Sj\u00f6str\u00f6ma M, Erikssonb L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intel Lab Syst 58:109\u2013130","journal-title":"Chemometr Intel Lab Syst"},{"key":"869_CR28","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273\u2013297","journal-title":"Mach Learn"},{"key":"869_CR29","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1021\/ci000459c","volume":"41","author":"FR Burden","year":"2001","unstructured":"Burden FR (2001) Quantitative structure-activity relationship studies using Gaussian processes. J Chem Inf Comput Sci 41:830\u2013835","journal-title":"J Chem Inf Comput Sci"},{"key":"869_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","volume":"185","author":"P Geladi","year":"1986","unstructured":"Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1\u201317","journal-title":"Anal Chim Acta"},{"key":"869_CR31","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1007\/s11434-006-0524-7","volume":"51","author":"P Zhou","year":"2006","unstructured":"Zhou P, Zhou Y, Wu S, Li B, Tian F, Li Z (2006) A new descriptor of amino acids based on the three-dimensional vector of atomic interaction field. Chin Sci Bull 51:524\u2013529","journal-title":"Chin Sci Bull"},{"key":"869_CR32","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s10822-012-9625-3","volume":"27","author":"P Zhou","year":"2013","unstructured":"Zhou P, Wang C, Tian F, Ren Y, Yang C, Huang J (2013) Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity. J Comput Aided Mol Des 27:67\u201378","journal-title":"J Comput Aided Mol Des"},{"key":"869_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2024.105191","volume":"252","author":"H Ye","year":"2024","unstructured":"Ye H, Zhang Y, Li Z, Peng Y, Zhou P (2024) Comprehensive evaluation and systematic comparison of Gaussian process (GP) modelling applications in peptide quantitative structure-activity relationship. Chemometr Intel Lab Syst 252:105191","journal-title":"Chemometr Intel Lab Syst"},{"key":"869_CR34","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1021\/ci7000633","volume":"47","author":"O Obrezanova","year":"2007","unstructured":"Obrezanova O, Cs\u00e1nyi G, Gola JM, Segall MD (2007) Gaussian processes: a method for automatic QSAR modeling of ADME properties. J Chem Inf Mol 47:1847\u20131857","journal-title":"J Chem Inf Mol"},{"key":"869_CR35","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1137\/1011036","volume":"11","author":"P Wolfe","year":"1969","unstructured":"Wolfe P (1969) Convergence conditions for ascent methods. SIAM Rev 11:226\u2013235","journal-title":"SIAM Rev"},{"key":"869_CR36","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.3390\/molecules26041111","volume":"26","author":"A R\u00e1cz","year":"2021","unstructured":"R\u00e1cz A, Bajusz D, H\u00e9berger K (2021) Effect of dataset size and train\/test split ratios in QSAR\/QSPR multiclass classification. Molecules 26:1111","journal-title":"Molecules"},{"key":"869_CR37","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/s00726-011-1101-1","volume":"43","author":"F Tian","year":"2012","unstructured":"Tian F, Lv Y, Yang L (2012) Structure-based prediction of protein-protein binding affinity with consideration of allosteric effect. Amino Acids 43:531\u2013543","journal-title":"Amino Acids"},{"key":"869_CR38","doi-asserted-by":"publisher","first-page":"3107","DOI":"10.1016\/j.chroma.2009.01.086","volume":"1216","author":"P Zhou","year":"2009","unstructured":"Zhou P, Tian F, Lv F, Shang Z (2009) Comprehensive comparison of eight statistical modelling methods used in quantitative structure\u2013retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome. J Chromatogr A 1216:3107\u20133116","journal-title":"J Chromatogr A"},{"key":"869_CR39","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.1021\/jm00390a003","volume":"30","author":"S Hellberg","year":"1987","unstructured":"Hellberg S, Sjoestroem M, Skagerberg B, Wold S (1987) Peptide quantitative structure-activity relationships, a multivariate approach. J Med Chem 30:1126\u20131135","journal-title":"J Med Chem"},{"key":"869_CR40","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.molstruc.2006.07.004","volume":"830","author":"F Tian","year":"2007","unstructured":"Tian F, Zhou P, Li Z (2007) T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides. J Mol Struct 830:106\u2013115","journal-title":"J Mol Struct"},{"key":"869_CR41","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/s00726-008-0116-8","volume":"36","author":"F Tian","year":"2009","unstructured":"Tian F, Yang L, Lv F, Yang Q, Zhou P (2009) In silico quantitative prediction of peptides binding affinity to human MHC molecule: an intuitive quantitative structure-activity relationship approach. Amino Acids 36:535\u2013554","journal-title":"Amino Acids"},{"key":"869_CR42","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/S1093-3263(01)00123-1","volume":"20","author":"A Golbraikh","year":"2002","unstructured":"Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20:269\u2013276","journal-title":"J Mol Graph Model"},{"key":"869_CR43","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1002\/qsar.200390007","volume":"22","author":"A Tropsha","year":"2003","unstructured":"Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69\u201377","journal-title":"QSAR Comb Sci"},{"key":"869_CR44","volume":"1","author":"Q Liu","year":"2021","unstructured":"Liu Q, Lin J, Wen L, Wang S, Zhou P, Mei L, Shang S (2021) Systematic modeling, prediction, and comparison of domain\u2013peptide affinities: does it work effectively with the peptide QSAR methodology? Front Genet 1:800857","journal-title":"Front Genet"},{"key":"869_CR45","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s10822-005-3993-x","volume":"19","author":"IA Doytchinova","year":"2005","unstructured":"Doytchinova IA, Walshe V, Borrow P, Flower DR (2005) Towards the chemometric dissection of peptide\u2013HLA-A*0201 binding affinity: comparison of local and global QSAR models. J Comput Aided Mol Des 19:203\u2013212","journal-title":"J Comput Aided Mol Des"},{"key":"869_CR46","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/s10930-020-09960-7","volume":"40","author":"Z Ni","year":"2021","unstructured":"Ni Z, Wang A, Kang L, Zhang T (2021) QSSR modeling of Bacillus subtilis lipase A peptide collision cross-sections in ion mobility spectrometry: local descriptor versus global descriptor. Protein J 40:54\u201362","journal-title":"Protein J"},{"key":"869_CR47","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1039\/cs9952400279","volume":"24","author":"AR Katritzky","year":"1995","unstructured":"Katritzky AR, Lobanov VS, Karelson M (1995) Chem Soc Rev 24:279\u2013287","journal-title":"Chem Soc Rev"},{"key":"869_CR48","doi-asserted-by":"publisher","first-page":"2159","DOI":"10.1002\/jssc.200800739","volume":"32","author":"F Tian","year":"2009","unstructured":"Tian F, Yang L, Lv F, Zhou P (2009) Modeling and prediction of retention behavior of histidine-containing peptides in immobilized metal-affinity chromatography. J Sep Sci 32:2159\u20132169","journal-title":"J Sep Sci"},{"key":"869_CR49","doi-asserted-by":"publisher","first-page":"266","DOI":"10.2174\/0113894501401057250928032123","volume":"27","author":"Y Guo","year":"2026","unstructured":"Guo Y, Liang Y, Liu M, Zhou J, Zhai Y, Wu Y, Wang X, Li D, Wu J, Xia S, Zuo Y (2026) Clinical deployment of interpretable AI: bridging routine clinical tests and proteomic signatures for preeclampsia risk stratification. Curr Drug Targets 27:266\u2013280","journal-title":"Curr Drug Targets"},{"key":"869_CR50","doi-asserted-by":"publisher","first-page":"921","DOI":"10.2174\/0113894501394785250715165404","volume":"26","author":"K Rajpoot","year":"2025","unstructured":"Rajpoot K (2025) Role of artificial intelligence in nanomedicine and organ-specific therapy: an updated review. Curr Drug Targets 26:921\u2013953","journal-title":"Curr Drug Targets"},{"key":"869_CR51","doi-asserted-by":"publisher","first-page":"100014","DOI":"10.1016\/j.abiote.2025.100014","volume":"7","author":"X Liu","year":"2025","unstructured":"Liu X, Guan F, Luo H, Yao B, Tian J (2025) Artificial intelligence-driven discovery of bioactive peptides: computational approaches and future perspectives. aBIOTECH 7:100014","journal-title":"aBIOTECH"}],"container-title":["Journal of Computer-Aided Molecular Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-026-00869-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10822-026-00869-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-026-00869-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T19:02:55Z","timestamp":1782414175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10822-026-00869-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,25]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["869"],"URL":"https:\/\/doi.org\/10.1007\/s10822-026-00869-4","relation":{},"ISSN":["1573-4951"],"issn-type":[{"value":"1573-4951","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,25]]},"assertion":[{"value":"22 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 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 conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"All authors have read and understood the publishing policy, and this manuscript is submitted in accordance with this policy.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"160"}}