{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T21:11:28Z","timestamp":1790716288814,"version":"4.1.0"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The growing threat of antimicrobial resistance (AMR) necessitates the rapid discovery of novel antimicrobial peptides (AMPs) as alternative therapeutics. However, most computational approaches rely on binary AMP or non-AMP classification or permissive MIC thresholds (e.g. \u2264128\u00a0\u03bcg\/mL), offering limited biological interpretability and translational value. We present CVAE-BIO, a biochemical-knowledge-driven, multi-module pipeline for the discovery of AMPs targeting drug-resistant Escherichia coli as a model pathogen yet generalisable to other bacterial targets. The model integrates a conditional variational autoencoder (CVAE) constrained by key biochemical properties (MIC\u226410\u00a0\u03bcg\/mL, net charge &amp;gt;\u2009+\u20092, peptide length\u2009&amp;lt;\u200940 residues, instability index &amp;lt;40, and Boman index &amp;lt;0) with a Random Forest classifier trained on 30 biochemical descriptors. In vitro validation showed that 18.5% of generated peptides exhibited strong activity (MIC\u226410\u00a0\u03bcg\/mL), with 38.9% reaching MIC \u226450\u00a0\u03bcg\/mL while maintaining key biochemical properties. Most validated novel peptides are narrow-spectrum AMP targeting E. coli. Wet-lab results also showed that highly active cationic-amphipathic AMPs are characterized by significantly low counts of tiny and small residues, suggesting that avoiding using these residues or limiting them to a maximum of 2 and 3, respectively, might improve the activity of AMP. Taking both antimicrobial activity and hemolytic toxicity into account, 9 peptides were identified as non-toxic and active AMP candidates. This explainable framework enables efficient AMP discovery under biochemical constraints and yields experimentally validated candidates with translational potential.<\/jats:p>","DOI":"10.1093\/bib\/bbag115","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T15:36:55Z","timestamp":1773848215000},"source":"Crossref","is-referenced-by-count":4,"title":["Biochemical-knowledge-driven machine learning pipeline for generating potent antimicrobial peptides"],"prefix":"10.1093","volume":"27","author":[{"given":"Deliang","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong , 102 Pok Fu Lam Road, Hong Kong SAR 00001 ,","place":["China"]},{"name":"The University of Hong Kong\u2013Shenzhen Hospital , No. 1, Haiyuan First Road, Futian District, Shenzhen, Guangdong, 518000 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong , 102 Pok Fu Lam Road, Hong Kong SAR 00001 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxi","family":"Li","sequence":"additional","affiliation":[{"name":"Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong , 34 Hospital Road, Hong Kong SAR 00001 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6819-0686","authenticated-orcid":false,"given":"Qingpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong , 21 Sassoon Road, Hong Kong SAR 00001 ,","place":["China"]},{"name":"Musketeers Foundation Institute of Data Science, The University of Hong Kong , Pok Fu Lam Road, Hong Kong SAR 00001 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7531-2816","authenticated-orcid":false,"given":"Jiandong","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Biomedical Sciences and School of Biomedical Engineering, Li Ka Shing Faculty of Medicine, The University of Hong Kong , 21 Sassoon Road, Hong Kong SAR 00001 ,","place":["China"]},{"name":"Materials Innovation Institute for Life Sciences and Energy (MILES), HKU\u2013SIRI , Shenzhen Virtual University Park, No. 6 Yuexing Second Road, Nanshan District, Shenzhen, Guangdong, 518000 ,","place":["China"]},{"name":"Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, Guangdong, 518000 ,","place":["China"]},{"name":"Centre for Virology, Vaccinology and Therapeutics , Hong Kong Science and Technology Park, Hong Kong SAR 00001 ,","place":["China"]},{"name":"Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, Department of Clinical Oncology, The University of Hong Kong-Shenzhen Hospital , No. 1, Haiyuan First Road, Futian District, Shenzhen, Guangdong, 518000 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4836-7808","authenticated-orcid":false,"given":"Xue","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong , 102 Pok Fu Lam Road, Hong Kong SAR 00001 ,","place":["China"]},{"name":"The University of Hong Kong\u2013Shenzhen Hospital , No. 1, Haiyuan First Road, Futian District, Shenzhen, Guangdong, 518000 ,","place":["China"]},{"name":"Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong , 21 Sassoon Road, Hong Kong SAR 00001 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6877-6388","authenticated-orcid":false,"given":"Peng","family":"Gao","sequence":"additional","affiliation":[{"name":"Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong , 34 Hospital Road, Hong Kong SAR 00001 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"2026031811364779400_ref1","first-page":"392","article-title":"Machine learning for antimicrobial peptide identification and design. Nature reviews","volume":"2","author":"Wan","year":"2024","journal-title":"Bioengineering."},{"key":"2026031811364779400_ref2","volume-title":"Global Priority List of Antibiotic-Resistant Bacteria to Guide Research, Discovery, and Development 2017","author":"(WHO) WHO"},{"key":"2026031811364779400_ref3","volume-title":"Review on Antimicrobial Resistance: Tackling Drug-Resistant Infections Globally: Final Report and Recommendations","author":"O'Neill","year":"2016"},{"key":"2026031811364779400_ref4","doi-asserted-by":"publisher","first-page":"9221","DOI":"10.1039\/d1sc01713f","article-title":"Machine learning designs non-hemolytic antimicrobial peptides","volume":"12","author":"Capecchi","year":"2021","journal-title":"Chem Sci"},{"key":"2026031811364779400_ref5","doi-asserted-by":"crossref","first-page":"117774","DOI":"10.1016\/j.bmc.2024.117774","article-title":"A consensus reverse docking approach for identification of a competitive inhibitor of acetyltransferase enhanced intracellular survival protein from mycobacterium tuberculosis","volume":"108","author":"Santos-J\u00fanior","year":"2024","journal-title":"Bioorg Med Chem"},{"key":"2026031811364779400_ref6","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/nrd3591","article-title":"Designing antimicrobial peptides: Form follows function","volume":"11","author":"Fjell","year":"2012","journal-title":"Nat Rev Drug Discov"},{"key":"2026031811364779400_ref7","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1038\/s41597-019-0154-y","article-title":"DRAMP 2.0, an updated data repository of antimicrobial peptides","volume":"6","author":"Kang","year":"2019","journal-title":"Sci Data"},{"key":"2026031811364779400_ref8","doi-asserted-by":"crossref","first-page":"194","DOI":"10.3389\/fcimb.2016.00194","article-title":"Antimicrobial peptides: An emerging category of therapeutic agents","volume":"6","author":"Mahlapuu","year":"2016","journal-title":"Front Cell Infect Microbiol"},{"key":"2026031811364779400_ref9","doi-asserted-by":"publisher","first-page":"22847","DOI":"10.1021\/acsomega.0c02088","article-title":"Generating ampicillin-level antimicrobial peptides with activity-aware generative adversarial networks. ACS","volume":"5","author":"Tucs","year":"2020","journal-title":"Omega."},{"key":"2026031811364779400_ref10","doi-asserted-by":"publisher","first-page":"2198","DOI":"10.1021\/acs.jcim.0c01441","article-title":"AMPGAN v2: Machine learning-guided Design of Antimicrobial Peptides","volume":"61","author":"Van Oort","year":"2021","journal-title":"J Chem Inf Model"},{"key":"2026031811364779400_ref11","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1038\/s41467-023-36994-z","article-title":"Discovering highly potent antimicrobial peptides with deep generative model HydrAMP","volume":"14","author":"Szymczak","year":"2023","journal-title":"Nat Commun"},{"key":"2026031811364779400_ref12","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1038\/s41551-021-00689-x","article-title":"Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations","volume":"5","author":"Das","year":"2021","journal-title":"Nat Bio Eng"},{"key":"2026031811364779400_ref13","doi-asserted-by":"publisher","DOI":"10.1093\/bioadv\/vbac021","article-title":"LMPred: Predicting antimicrobial peptides using pre-trained language models and deep learning","volume":"2","author":"Dee","journal-title":"Bioinformatics Advances"},{"key":"2026031811364779400_ref14","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad240","article-title":"iAMPCN: A deep-learning approach for identifying antimicrobial peptides and their functional activities","volume":"24","author":"Xu","journal-title":"Briefings in Bioinformatics"},{"key":"2026031811364779400_ref15","doi-asserted-by":"publisher","DOI":"10.3390\/genes12020137","article-title":"Ensemble-AMPPred: Robust AMP prediction and recognition using the ensemble learning method with a new hybrid feature for differentiating AMPs","volume":"12","author":"Lertampaiporn","year":"2021","journal-title":"Genes"},{"key":"2026031811364779400_ref16","doi-asserted-by":"publisher","first-page":"3761","DOI":"10.1016\/j.cell.2024.05.013","article-title":"Discovery of antimicrobial peptides in the global microbiome with machine learning","volume":"187","author":"Santos-J\u00fanior","year":"2024","journal-title":"Cell."},{"key":"2026031811364779400_ref17","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1038\/s41551-022-00991-2","article-title":"Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences","volume":"7","author":"Huang","year":"2023","journal-title":"Nat Biomed Eng"},{"key":"2026031811364779400_ref18","doi-asserted-by":"publisher","first-page":"D1087","DOI":"10.1093\/nar\/gkv1278","article-title":"APD3: The antimicrobial peptide database as a tool for research and education","volume":"44","author":"Wang","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2026031811364779400_ref19","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1093\/protein\/4.2.155","article-title":"Correlation between stability of a protein and its dipeptide composition: A novel approach for predicting in vivo stability of a protein from its primary sequence","volume":"4","author":"Guruprasad","year":"1990","journal-title":"Protein Eng"},{"key":"2026031811364779400_ref20","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1046\/j.1365-2796.2003.01228.x","article-title":"Antibacterial peptides: Basic facts and emerging concepts","volume":"254","author":"Boman","year":"2003","journal-title":"J Intern Med"},{"key":"2026031811364779400_ref21","article-title":"Learning latent subspaces in variational autoencoders","volume":"31","author":"Klys","year":"2018","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2026031811364779400_ref22","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Devlin"},{"key":"2026031811364779400_ref23","first-page":"Article 140","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J Mach Learn Res"},{"key":"2026031811364779400_ref24","doi-asserted-by":"publisher","DOI":"10.3390\/ijms222212520","article-title":"Antiseptic 9-Meric peptide with potency against Carbapenem-resistant Acinetobacter baumannii infection","volume":"22","author":"Krishnan","year":"2021","journal-title":"Int J Mol Sci"},{"key":"2026031811364779400_ref25","doi-asserted-by":"publisher","first-page":"55704","DOI":"10.1021\/acsomega.5c07032","article-title":"In vitro properties of WMRK-gH625, a novel hybrid peptide against multidrug-resistant pathogens","volume":"10","author":"Scaglione","year":"2025","journal-title":"ACS Omega"},{"key":"2026031811364779400_ref26","doi-asserted-by":"publisher","first-page":"10488","DOI":"10.1038\/s41467-025-65449-w","article-title":"An antimicrobial peptide as a potential therapy for bacterial pneumonia that alleviates antimicrobial resistance","volume":"16","author":"Zhong","year":"2025","journal-title":"Nat Commun"},{"key":"2026031811364779400_ref27","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.cocis.2009.02.004","article-title":"Rationalizing the membrane interactions of cationic amphipathic antimicrobial peptides by their molecular shape","volume":"14","author":"Bechinger","year":"2009","journal-title":"Curr Opin Colloid Interface Sci"},{"key":"2026031811364779400_ref28","doi-asserted-by":"publisher","first-page":"2043","DOI":"10.1016\/j.bbagen.2018.06.011","article-title":"Joker: An algorithm to insert patterns into sequences for designing antimicrobial peptides","volume":"1862","author":"Porto","year":"2018","journal-title":"Biochimica et biophysica acta General subjects"},{"key":"2026031811364779400_ref29","doi-asserted-by":"publisher","first-page":"3184","DOI":"10.1038\/s41467-020-16950-x","article-title":"An amphipathic peptide with antibiotic activity against multidrug-resistant gram-negative bacteria","volume":"11","author":"Elliott","year":"2020","journal-title":"Nat Commun"},{"key":"2026031811364779400_ref30","doi-asserted-by":"publisher","first-page":"4817","DOI":"10.1021\/acs.jpcb.7b03067","article-title":"Glycine substitution Reducesg antimicrobial activity and helical stretch of diPGLa-H in lipid micelles","volume":"121","author":"Sani","year":"2017","journal-title":"J Phys Chem B"},{"key":"2026031811364779400_ref31","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1002\/cbic.201700599","article-title":"Alanine and lysine scans of the LL-37-derived peptide fragment KR-12 reveal key residues for antimicrobial activity","volume":"19","author":"Gunasekera","year":"2018","journal-title":"Chembiochem."},{"key":"2026031811364779400_ref32","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/415389a","article-title":"Antimicrobial peptides of multicellular organisms","volume":"415","author":"Zasloff","year":"2002","journal-title":"Nature."},{"key":"2026031811364779400_ref33","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1073\/pnas.81.1.140","article-title":"The hydrophobic moment detects periodicity in protein hydrophobicity","volume":"81","author":"Eisenberg","year":"1984","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2026031811364779400_ref34","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/S0168-9525(00)02024-2","article-title":"EMBOSS: The European molecular biology open software suite","volume":"16","author":"Rice","year":"2000","journal-title":"Trends Genet"},{"key":"2026031811364779400_ref35","doi-asserted-by":"publisher","first-page":"20746","DOI":"10.1021\/acsomega.0c00442","article-title":"Variational autoencoder for generation of antimicrobial peptides","volume":"5","author":"Dean","year":"2020","journal-title":"ACS Omega"},{"key":"2026031811364779400_ref36","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013","journal-title":"arXiv preprint arXiv:13126114"},{"key":"2026031811364779400_ref37","doi-asserted-by":"publisher","first-page":"i610","DOI":"10.1093\/bioinformatics\/btaa800","article-title":"Conditional out-of-distribution generation for unpaired data using transfer VAE","volume":"36","author":"Lotfollahi","year":"2020","journal-title":"Bioinformatics."}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/2\/bbag115\/67391417\/bbag115.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/2\/bbag115\/67391417\/bbag115.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T15:36:58Z","timestamp":1773848218000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbag115\/8527775"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,1]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,3,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbag115","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,3]]},"published":{"date-parts":[[2026,3,1]]},"article-number":"bbag115"}}