{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:06:31Z","timestamp":1779253591751,"version":"3.51.4"},"reference-count":131,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Antibiotics"],"abstract":"<jats:p>The escalating crisis of antimicrobial resistance claims nearly 5 million lives annually. Resistant infections now account for 4.95 million deaths worldwide and economic losses projected to reach $300 billion by 2030. Despite this urgent threat, traditional antibiotic discovery has declined precipitously. New chemical entity approvals have fallen by over 50%, while existing therapeutics are rapidly rendered obsolete by sophisticated bacterial resistance mechanisms including extended-spectrum \u03b2-lactamases, carbapenemases, and multidrug efflux pumps. Bio-based products have historically provided humanity\u2019s most transformative antibiotics, yet conventional discovery pipelines face insurmountable bottlenecks. A total of 99.9% of environmental microbes remain unculturable. Biosynthetic gene clusters are predominantly silent under laboratory conditions, and dereplication efforts achieve only 2 to 5% annotation rates. This review presents a comprehensive examination of how artificial intelligence (AI) is revolutionizing bio-based product-based antibacterial discovery. We analyze AI-driven genome mining tools that have identified over 170,000 biosynthetic gene clusters across bacterial genomes, deep learning architectures achieving 88.5% bioactivity prediction accuracy, and generative models delivering experimental hit rates exceeding 50%\u2014representing 50- to 90-fold improvements over traditional screening. Through validated case studies spanning in silico prediction to in vivo efficacy, we demonstrate that AI integration is not merely accelerating discovery but fundamentally transforming our capacity to access nature\u2019s previously inaccessible chemical diversity in the fight against antimicrobial resistance.<\/jats:p>","DOI":"10.3390\/antibiotics15050478","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:11:13Z","timestamp":1778271073000},"page":"478","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development"],"prefix":"10.3390","volume":"15","author":[{"given":"Renuka","family":"Gudepu","sequence":"first","affiliation":[{"name":"Department of Microbiology, Pingle Government College for Women (A), Warangal 506370, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Swapna","family":"Sirikonda","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutics, School of Pharmacy, Anurag University, Hyderabad 500088, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravinaik","family":"Banoth","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, St. Martin\u2019s Engineering College, Secunderabad 500100, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Praveen Kumar","family":"Annagowni","sequence":"additional","affiliation":[{"name":"Department of Pharmaceuticals Sciences, Jawaharlal Nehru Technology University, Anantapur 515002, Andhra Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8361-3897","authenticated-orcid":false,"given":"Swati","family":"Dahariya","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad 500019, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0969-6142","authenticated-orcid":false,"given":"Aditya","family":"Velidandi","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Vaagdevi Degree and P.G. College, Warangal 506001, Telangana, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e897","DOI":"10.1016\/S2468-2667(22)00225-0","article-title":"The Burden of Bacterial Antimicrobial Resistance in the WHO European Region in 2019: A Cross-Country Systematic Analysis","volume":"7","author":"Mestrovic","year":"2022","journal-title":"Lancet Public Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"151","DOI":"10.18081\/2333-5106\/2024.12\/151","article-title":"Comprehensive Assessment of the Global Burden of Antimicrobial Resistance: Trends and Insights from 2000 to 2023","volume":"12","author":"Alomari","year":"2024","journal-title":"Am. J. Biomed."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Balasubramanian, R., Van Boeckel, T.P., Carmeli, Y., Cosgrove, S., and Laxminarayan, R. (2023). Global Incidence in Hospital-Associated Infections Resistant to Antibiotics: An Analysis of Point Prevalence Surveys from 99 Countries. PLoS Med., 20.","DOI":"10.1371\/journal.pmed.1004178"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e016249","DOI":"10.1136\/bmjgh-2024-016249","article-title":"The Global Economic Burden of Antibiotic-Resistant Infections and the Potential Impact of Bacterial Vaccines: A Modelling Study","volume":"10","author":"Naylor","year":"2025","journal-title":"BMJ Glob. Health"},{"key":"ref_5","first-page":"47","article-title":"Assessing Infection Patterns, Resistant Pathogens and Targeted Bacterial Mechanisms: A Comparative Analysis of Antimicrobial Resistance in Five Countries","volume":"VIII","author":"Ayesha","year":"2023","journal-title":"Glob. Pharm. Sci. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13756-020-00872-w","article-title":"Economic Burden of Antibiotic Resistance in China: A National Level Estimate for Inpatients","volume":"10","author":"Zhen","year":"2021","journal-title":"Antimicrob. Resist. Infect. Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s13756-018-0384-3","article-title":"Enumerating the Economic Cost of Antimicrobial Resistance per Antibiotic Consumed to Inform the Evaluation of Interventions Affecting Their Use","volume":"7","author":"Shrestha","year":"2018","journal-title":"Antimicrob. Resist. Infect. Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1007\/s40121-023-00837-7","article-title":"Clinical and Economic Value of Reducing Antimicrobial Resistance in the Management of Hospital-Acquired Infections with Limited Treatment Options in Greece","volume":"12","author":"Barmpouni","year":"2023","journal-title":"Infect. Dis. Ther."},{"key":"ref_9","first-page":"2200672","article-title":"Clinical Epidemiology and Case Fatality Due to Antimicrobial Resistance in Germany: A Systematic Review and Meta-Analysis, 1 January 2010 to 31 December 2021","volume":"28","author":"Ayobami","year":"2023","journal-title":"Eurosurveillance"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Iskandar, K., Roques, C., Hallit, S., Husni-Samaha, R., Dirani, N., Rizk, R., Abdo, R., Yared, Y., Matta, M., and Mostafa, I. (2021). The Healthcare Costs of Antimicrobial Resistance in Lebanon: A Multi-Centre Prospective Cohort Study from the Payer Perspective. BMC Infect. Dis., 21.","DOI":"10.1186\/s12879-021-06084-w"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Founou, R.C., Founou, L.L., and Essack, S.Y. (2017). Clinical and Economic Impact of Antibiotic Resistance in Developing Countries: A Systematic Review and Meta-Analysis. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0189621"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kakoullis, L., Papachristodoulou, E., Chra, P., and Panos, G. (2021). Mechanisms of Antibiotic Resistance in Important Gram-Positive and Gram-Negative Pathogens and Novel Antibiotic Solutions. Antibiotics, 10.","DOI":"10.3390\/antibiotics10040415"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"300","DOI":"10.4103\/joacp.JOACP_349_15","article-title":"Action and Resistance Mechanisms of Antibiotics: A Guide for Clinicians","volume":"33","author":"Kapoor","year":"2017","journal-title":"J. Anaesthesiol. Clin. Pharmacol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"557","DOI":"10.2165\/00003495-200262040-00001","article-title":"Mechanisms of Antimicrobial Resistance","volume":"62","author":"Sefton","year":"2002","journal-title":"Drugs"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1111\/nyas.14254","article-title":"Molecular Epidemiology and Mechanisms of Antibiotic Resistance in Enterococcus Spp., Staphylococcus Spp., and Streptococcus Spp. in Africa: A Systematic Review from a One Health Perspective","volume":"1465","author":"Mensah","year":"2020","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1089\/mdr.2020.0001","article-title":"Correlation Between Biofilm Formation and Antibiotic Resistance in MRSA and MSSA Isolated from Clinical Samples in Iran: A Systematic Review and Meta-Analysis","volume":"26","author":"Hosseini","year":"2020","journal-title":"Microb. Drug Resist."},{"key":"ref_17","first-page":"51","article-title":"Global Patterns of Antibiotic Resistance Development in Common Bacterial Infections and Their Implications for Treatment Strategies: A Systematic Review","volume":"3","author":"Zainab","year":"2025","journal-title":"Insights-J. Life Soc. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103161","DOI":"10.1016\/j.jiph.2026.103161","article-title":"Risk Factors, Clinical Outcomes of Patients with Ceftazidime\/Avibactam-Resistant Carbapenem-Resistant Klebsiella Pneumoniae Infection and Its Potential Resistant Mechanisms","volume":"19","author":"Wu","year":"2026","journal-title":"J. Infect. Public Health"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100887","DOI":"10.37349\/eds.2025.100887","article-title":"Combating Antibiotic Resistance: Mechanisms, Challenges, and Innovative Approaches in Antibacterial Drug Development","volume":"3","author":"Rajesh","year":"2025","journal-title":"Explor. Drug Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1007\/s10096-004-1254-x","article-title":"Ciprofloxacin Treatment Failure in a Patient with Resistant Streptococcus Pneumoniae Infection Following Prior Ciprofloxacin Therapy","volume":"24","author":"Pletz","year":"2005","journal-title":"Eur. J. Clin. Microbiol. Infect. Dis."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4754","DOI":"10.3390\/md13084754","article-title":"The Sound of Silence: Activating Silent Biosynthetic Gene Clusters in Marine Microorganisms","volume":"13","author":"Reen","year":"2015","journal-title":"Mar. Drugs"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jantan, I., Bukhari, S.N.A., Mohamed, M.A.S., Wai, L.K., and Mesaik, M.A. (2015). The Evolving Role of Natural Products from the Tropical Rainforests as a Replenishable Source of New Drug Leads. Drug Discovery and Development\u2014From Molecules to Medicine, InTech.","DOI":"10.5772\/59603"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gaud\u00eancio, S.P., Bayram, E., Luki\u0107 Bilela, L., Cueto, M., D\u00edaz-Marrero, A.R., Haznedaroglu, B.Z., Jimenez, C., Mandalakis, M., Pereira, F., and Reyes, F. (2023). Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation. Mar. Drugs, 21.","DOI":"10.3390\/md21050308"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s40828-021-00138-x","article-title":"Tetracycline Antibiotics","volume":"7","author":"Ramachanderan","year":"2021","journal-title":"ChemTexts"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1021\/acsinfecdis.3c00067","article-title":"Screen for New Antimicrobial Natural Products from the NCI Program for Natural Product Discovery Prefractionated Extract Library","volume":"9","author":"Arends","year":"2023","journal-title":"ACS Infect. Dis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1128\/AAC.01781-13","article-title":"Image-Based 384-Well High-Throughput Screening Method for the Discovery of Skyllamycins A to C as Biofilm Inhibitors and Inducers of Biofilm Detachment in Pseudomonas Aeruginosa","volume":"58","author":"Navarro","year":"2014","journal-title":"Antimicrob. Agents Chemother."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1038\/s41579-020-0379-y","article-title":"Ecology and Genomics of Actinobacteria: New Concepts for Natural Product Discovery","volume":"18","author":"Terlouw","year":"2020","journal-title":"Nat. Rev. Microbiol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, Z., Nielsen, J., and Liu, Z. (2020). Bioprospecting Through Cloning of Whole Natural Product Biosynthetic Gene Clusters. Front. Bioeng. Biotechnol., 8.","DOI":"10.3389\/fbioe.2020.00526"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Rafiq, M., Hassan, N., Rehman, M., Hayat, M., Nadeem, G., Hassan, F., Iqbal, N., Ali, H., Zada, S., and Kang, Y. (2023). Challenges and Approaches of Culturing the Unculturable Archaea. Biology, 12.","DOI":"10.3390\/biology12121499"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1111\/1751-7915.13782","article-title":"Accessing Previously Uncultured Marine Microbial Resources by a Combination of Alternative Cultivation Methods","volume":"14","author":"Jung","year":"2021","journal-title":"Microb. Biotechnol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7266","DOI":"10.1073\/pnas.1400019111","article-title":"High-Throughput Platform for the Discovery of Elicitors of Silent Bacterial Gene Clusters","volume":"111","author":"Seyedsayamdost","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_32","first-page":"276","article-title":"Activation of Microbial Silent Gene Clusters: Genomics Driven Drug Discovery Approaches","volume":"5","year":"2016","journal-title":"Biochem. Anal. Biochem."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Periwal, V., Bassler, S., Andrejev, S., Gabrielli, N., Patil, K.R., Typas, A., and Patil, K.R. (2022). Bioactivity Assessment of Natural Compounds Using Machine Learning Models Trained on Target Similarity between Drugs. PLoS Comput. Biol., 18.","DOI":"10.1371\/journal.pcbi.1010029"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"pgac257","DOI":"10.1093\/pnasnexus\/pgac257","article-title":"NPOmix: A Machine Learning Classifier to Connect Mass Spectrometry Fragmentation Data to Biosynthetic Gene Clusters","volume":"1","author":"Wang","year":"2022","journal-title":"PNAS Nexus"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"139231","DOI":"10.1016\/j.ijbiomac.2024.139231","article-title":"Identification and Mechanistic Study of Piceatannol as a Natural Xanthine Oxidase Inhibitor","volume":"293","author":"Yan","year":"2025","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"44757","DOI":"10.1021\/acsomega.3c05778","article-title":"BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity Representation","volume":"8","author":"Ghorbanali","year":"2023","journal-title":"ACS Omega"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3984","DOI":"10.1093\/bioinformatics\/btac420","article-title":"Improving Candidate Biosynthetic Gene Clusters in Fungi through Reinforcement Learning","volume":"38","author":"Almeida","year":"2022","journal-title":"Bioinformatics"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rios-Martinez, C., Bhattacharya, N., Amini, A.P., Crawford, L., and Yang, K.K. (2023). Deep Self-Supervised Learning for Biosynthetic Gene Cluster Detection and Product Classification. PLoS Comput. Biol., 19.","DOI":"10.1371\/journal.pcbi.1011162"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1007\/s10822-022-00486-x","article-title":"Enabling Data-Limited Chemical Bioactivity Predictions through Deep Neural Network Transfer Learning","volume":"36","author":"Liu","year":"2022","journal-title":"J. Comput. Aided. Mol. Des."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1021\/acs.jnatprod.3c00879","article-title":"PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning","volume":"87","author":"Gahl","year":"2024","journal-title":"J. Nat. Prod."},{"key":"ref_41","first-page":"1934578X2211173","article-title":"Deep Learning Model to Identify Potential Acetylcholinesterase Inhibitors: A Case Study of Isolated Compounds From Pongamia pinnata (L.) Pierre","volume":"17","author":"Nguyen","year":"2022","journal-title":"Nat. Prod. Commun."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Shin, S.H., Oh, S.M., Yoon Park, J.H., Lee, K.W., and Yang, H. (2022). OptNCMiner: A Deep Learning Approach for the Discovery of Natural Compounds Modulating Disease-Specific Multi-Targets. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-04752-5"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1038\/s41467-023-37570-1","article-title":"Predicting Compound Activity from Phenotypic Profiles and Chemical Structures","volume":"14","author":"Moshkov","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"31222","DOI":"10.1038\/s41598-024-82559-5","article-title":"Hybridisation of in Silico and in Vitro Bioassays for Studying the Activation of Nrf2 by Natural Compounds","volume":"14","author":"Nguyen","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"9910","DOI":"10.1021\/acs.jcim.5c00741","article-title":"Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction","volume":"65","author":"Gu","year":"2025","journal-title":"J. Chem. Inf. Model."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s10822-021-00376-8","article-title":"Evaluation of Multi-Target Deep Neural Network Models for Compound Potency Prediction under Increasingly Challenging Test Conditions","volume":"35","author":"Bajorath","year":"2021","journal-title":"J. Comput. Aided. Mol. Des."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"24764","DOI":"10.1038\/s41598-024-75487-x","article-title":"MetaCGRP Is a High-Precision Meta-Model for Large-Scale Identification of CGRP Inhibitors Using Multi-View Information","volume":"14","author":"Schaduangrat","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1038\/s41586-023-06887-8","article-title":"Discovery of a Structural Class of Antibiotics with Explainable Deep Learning","volume":"626","author":"Wong","year":"2024","journal-title":"Nature"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s12539-024-00670-7","article-title":"Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach","volume":"17","author":"Zhang","year":"2025","journal-title":"Interdiscip. Sci. Comput. Life Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"167597","DOI":"10.1016\/j.jmb.2022.167597","article-title":"Deep Learning to Predict the Biosynthetic Gene Clusters in Bacterial Genomes","volume":"434","author":"Liu","year":"2022","journal-title":"J. Mol. Biol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5356","DOI":"10.1038\/s41467-024-49587-1","article-title":"Discovering Type I Cis-AT Polyketides through Computational Mass Spectrometry and Genome Mining with Seq2PKS","volume":"15","author":"Yan","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s10886-023-01452-z","article-title":"A Multi-Label Learning Framework for Predicting Chemical Classes and Biological Activities of Natural Products from Biosynthetic Gene Clusters","volume":"49","author":"Mei","year":"2023","journal-title":"J. Chem. Ecol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"7193","DOI":"10.1021\/acs.jcim.5c00465","article-title":"Predicting Biological Activity from Biosynthetic Gene Clusters Using Neural Networks","volume":"65","author":"Goyat","year":"2025","journal-title":"J. Chem. Inf. Model."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"396","DOI":"10.2533\/chimia.2022.396","article-title":"Generating Bioactive Natural Product-Inspired Molecules with Machine Intelligence","volume":"76","author":"Schneider","year":"2022","journal-title":"Chimia"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1038\/s42256-026-01216-w","article-title":"Molecular Deep Learning at the Edge of Chemical Space","volume":"8","author":"Rossen","year":"2026","journal-title":"Nat. Mach. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e110","DOI":"10.1093\/nar\/gkz654","article-title":"A Deep Learning Genome-Mining Strategy for Biosynthetic Gene Cluster Prediction","volume":"47","author":"Hannigan","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Du, Z., Zhong, N., and Li, J. (2024). Enhancing Gene Cluster Identification and Classification in Bacterial Genomes Through Synonym Replacement and Deep Learning. Proceedings of the 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE.","DOI":"10.1109\/BIBM62325.2024.10821874"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"gkaf305","DOI":"10.1093\/nar\/gkaf305","article-title":"Deciphering the Biosynthetic Potential of Microbial Genomes Using a BGC Language Processing Neural Network Model","volume":"53","author":"Lai","year":"2025","journal-title":"Nucleic Acids Res."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Mishra, D.C., Madival, S.D., Sharma, A., Budhlakoti, N., Chaturvedi, K.K., Angadi, U.B., Farooqi, M.S., Srivastava, S., Basavaraja, P., and Arora, A. (2023). Enhancing the Classification of Biosynthetic Gene Clusters through Comprehensive NLP-Based Approach. Preprints, 2023101564.","DOI":"10.20944\/preprints202310.1564.v1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"130859","DOI":"10.1016\/j.bbagen.2025.130859","article-title":"RFBGCpred: A Random Forest Based Tool for Prediction of Biosynthetic Gene Clusters","volume":"1869","author":"Madival","year":"2025","journal-title":"Biochim. Biophys. Acta\u2014Gen. Subj."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Yadav, A., and Subramanian, S. (2024). HiFiBGC: An Ensemble Approach for Improved Biosynthetic Gene Cluster Detection in PacBio HiFi-Read Metagenomes. BMC Genom., 25.","DOI":"10.1186\/s12864-024-10950-7"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"35323","DOI":"10.1021\/jacs.5c08251","article-title":"Seq2Saccharide: Discovering Oligosaccharides and Aminoglycosides Natural Products by Integrating Computational Mass Spectrometry and Genome Mining","volume":"147","author":"Yan","year":"2025","journal-title":"J. Am. Chem. Soc."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zierep, P.F., Ceci, A.T., Dobrusin, I., Rockwell-Kollmann, S.C., and G\u00fcnther, S. (2020). SeMPI 2.0\u2014A Web Server for PKS and NRPS Predictions Combined with Metabolite Screening in Natural Product Databases. Metabolites, 11.","DOI":"10.3390\/metabo11010013"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"17506","DOI":"10.1039\/D4SC03469D","article-title":"Exploration, Expansion and Definition of the Atropopeptide Family of Ribosomally Synthesized and Posttranslationally Modified Peptides","volume":"15","author":"Biermann","year":"2024","journal-title":"Chem. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5478","DOI":"10.1093\/nar\/gkae314","article-title":"BGCFlow: Systematic Pangenome Workflow for the Analysis of Biosynthetic Gene Clusters across Large Genomic Datasets","volume":"52","author":"Nuhamunada","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"lqae069","DOI":"10.1093\/nargab\/lqae069","article-title":"Synteruptor: Mining Genomic Islands for Non-Classical Specialized Metabolite Gene Clusters","volume":"6","author":"Haas","year":"2024","journal-title":"NAR Genom. Bioinform."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Li, C., Sutherland, D., Hammond, S.A., Yang, C., Taho, F., Bergman, L., Houston, S., Warren, R.L., Wong, T., and Hoang, L.M.N. (2022). AMPlify: Attentive Deep Learning Model for Discovery of Novel Antimicrobial Peptides Effective against WHO Priority Pathogens. BMC Genom., 23.","DOI":"10.1186\/s12864-022-08310-4"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Rahman, A.S.M.Z., Liu, C., Sturm, H., Hogan, A.M., Davis, R., Hu, P., and Cardona, S.T. (2022). A Machine Learning Model Trained on a High-Throughput Antibacterial Screen Increases the Hit Rate of Drug Discovery. PLoS Comput. Biol., 18.","DOI":"10.1371\/journal.pcbi.1010613"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"RP97330","DOI":"10.7554\/eLife.97330.3","article-title":"Exploring the Repository of de Novo-Designed Bifunctional Antimicrobial Peptides through Deep Learning","volume":"13","author":"Dong","year":"2025","journal-title":"Elife"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"bbac162","DOI":"10.1093\/bib\/bbac162","article-title":"Improved Drug\u2013Target Interaction Prediction with Intermolecular Graph Transformer","volume":"23","author":"Liu","year":"2022","journal-title":"Brief. Bioinform."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"e13099","DOI":"10.1002\/advs.202513099","article-title":"DualPG-DTA: A Large Language Model-Powered Graph Neural Network Framework for Enhanced Drug-Target Affinity Prediction and Discovery of Novel CDK9 Inhibitors Exhibiting in Vivo Anti-Leukemia Activity","volume":"13","author":"Chen","year":"2026","journal-title":"Adv. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"3420","DOI":"10.1038\/s41467-025-58804-4","article-title":"Pre-Trained Molecular Representations Enable Antimicrobial Discovery","volume":"16","author":"Amstalden","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"21518","DOI":"10.1039\/D5SC03055B","article-title":"Transfer Learning Enables Discovery of Sub-Micromolar Antibacterials for ESKAPE Pathogens from Ultra-Large Chemical Spaces","volume":"16","author":"Seal","year":"2025","journal-title":"Chem. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Almatroudi, A. (2024). Integrative Machine Learning, Virtual Screening, and Molecular Modeling for BacA-Targeted Anti-Biofilm Drug Discovery Against Staphylococcal Infections. Crystals, 14.","DOI":"10.3390\/cryst14121057"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"3470","DOI":"10.1038\/s41467-024-47171-1","article-title":"Cell Painting-Based Bioactivity Prediction Boosts High-Throughput Screening Hit-Rates and Compound Diversity","volume":"15","author":"Lardeau","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"e70060","DOI":"10.1002\/jcc.70060","article-title":"Increase Docking Score Screening Power by Simple Fusion With CNNscore","volume":"46","author":"Liang","year":"2025","journal-title":"J. Comput. Chem."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"116776","DOI":"10.1016\/j.ejmech.2024.116776","article-title":"MalariaFlow: A Comprehensive Deep Learning Platform for Multistage Phenotypic Antimalarial Drug Discovery","volume":"277","author":"Lin","year":"2024","journal-title":"Eur. J. Med. Chem."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"51271","DOI":"10.1021\/acsomega.4c07994","article-title":"Deep Multitask Learning-Driven Discovery of New Compounds Targeting Leishmania Infantum","volume":"9","author":"Santos","year":"2024","journal-title":"ACS Omega"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Castaldi, A., Truong, B.N., Vu, Q.T., Le, T.H.M., Marie, A., Le Pennec, G., Rouvier, F., Brunel, J.-M., Longeon, A., and Pham, V.C. (2024). Computational Methods Reveal a Series of Cyclic and Linear Lichenysins and Surfactins from the Vietnamese Marine Sediment-Derived Streptomyces Strain G222. Molecules, 29.","DOI":"10.3390\/molecules29071458"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1007\/s10811-023-02906-3","article-title":"Application of Feature-Based Molecular Networking in the Field of Algal Research with Special Focus on Mycosporine-like Amino Acids","volume":"35","author":"Zwerger","year":"2023","journal-title":"J. Appl. Phycol."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Dofini Magnini, R., Pedinielli, F., Vergalli, J., Ouedraogo, N., Remy, S., Hilou, A., Brunel, J.-M., Pag\u00e8s, J.-M., and Davin-Regli, A. (2023). Acacia Senegal Budmunchiamines as a Potential Adjuvant for Rejuvenating Phenicol Activities towards Escherichia Coli-Resistant Strains. Int. J. Mol. Sci., 24.","DOI":"10.3390\/ijms24108790"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1007\/s00216-025-06262-8","article-title":"Mapping the Unknown: An Anchor-Based Molecular Networking Workflow for Comprehensive Identification of New Psychoactive Substances and Analogues","volume":"418","author":"Labat","year":"2026","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1128\/msystems.00356-23","article-title":"Functional Metabolomics of the Human Scalp: A Metabolic Niche for Staphylococcus Epidermidis","volume":"9","author":"Nothias","year":"2024","journal-title":"mSystems"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s13321-023-00724-w","article-title":"UmetaFlow: An Untargeted Metabolomics Workflow for High-Throughput Data Processing and Analysis","volume":"15","author":"Kontou","year":"2023","journal-title":"J. Cheminform."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13321-023-00695-y","article-title":"MAW: The Reproducible Metabolome Annotation Workflow for Untargeted Tandem Mass Spectrometry","volume":"15","author":"Zulfiqar","year":"2023","journal-title":"J. Cheminform."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"13350","DOI":"10.1021\/acs.analchem.5c01594","article-title":"CSU-MS 2: A Contrastive Learning Framework for Cross-Modal Compound Identification from MS\/MS Spectra to Molecular Structures","volume":"97","author":"Xie","year":"2025","journal-title":"Anal. Chem."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1007\/s00216-025-06246-8","article-title":"Efficient Profiling of Dimethoate Impurities via Integrated Platform Coupling Chromatography with Mass Spectrometry and Computer-Aided Annotation","volume":"418","author":"Hu","year":"2026","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"e202400088","DOI":"10.1002\/cmtd.202400088","article-title":"MS2DECIDE: Aggregating Multiannotated Tandem Mass Spectrometry Data with Decision Theory Enhances Natural Products Prioritization","volume":"5","author":"Mejri","year":"2025","journal-title":"Chem. \u2013Methods"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1038\/s41587-025-02663-3","article-title":"Self-Supervised Learning of Molecular Representations from Millions of Tandem Mass Spectra Using DreaMS","volume":"44","author":"Bushuiev","year":"2025","journal-title":"Nat. Biotechnol."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"bbaf333","DOI":"10.1093\/bib\/bbaf333","article-title":"SingleFrag: A Deep Learning Tool for MS\/MS Fragment and Spectral Prediction and Metabolite Annotation","volume":"26","author":"Badia","year":"2025","journal-title":"Brief. Bioinform."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"18326","DOI":"10.1021\/acs.analchem.3c02413","article-title":"Deep Learning-Enabled MS\/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances","volume":"95","author":"Wang","year":"2023","journal-title":"Anal. Chem."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"11102","DOI":"10.1038\/s41467-025-66060-9","article-title":"FIDDLE: A Deep Learning Method for Chemical Formulas Prediction from Tandem Mass Spectra","volume":"16","author":"Hong","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"bbag054","DOI":"10.1093\/bib\/bbag054","article-title":"ChemEmbed: A Deep Learning Framework for Metabolite Identification Using Enhanced MS\/MS Data and Multidimensional Molecular Embeddings","volume":"27","author":"Badia","year":"2026","journal-title":"Brief. Bioinform."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1038\/s42004-023-00932-3","article-title":"An End-to-End Deep Learning Framework for Translating Mass Spectra to de-Novo Molecules","volume":"6","author":"Litsa","year":"2023","journal-title":"Commun. Chem."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, X., Zhao, W., Zhu, D., and Cui, X. (2023). De Novo Molecular Structure Generation from Mass Spectra. Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE.","DOI":"10.1109\/BIBM58861.2023.10385903"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1038\/s42256-023-00708-3","article-title":"Annotating Metabolite Mass Spectra with Domain-Inspired Chemical Formula Transformers","volume":"5","author":"Goldman","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Sukor, N.S.M., Zakri, Z.H.M., Rasol, N.E., and Salim, F. (2023). Annotation and Identification of Phytochemicals from Eleusine Indica Using High-Performance Liquid Chromatography Tandem Mass Spectrometry: Databases-Driven Approach. Molecules, 28.","DOI":"10.3390\/molecules28073111"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40168-022-01444-3","article-title":"Enhanced Correlation-Based Linking of Biosynthetic Gene Clusters to Their Metabolic Products through Chemical Class Matching","volume":"11","author":"Louwen","year":"2023","journal-title":"Microbiome"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"146361","DOI":"10.1016\/j.foodchem.2025.146361","article-title":"Molecular Networking and Deep Learning Synergy for Bioactive Metabolite Discovery in L. Plantarum\u2013Fermented Sea Buckthorn Milk","volume":"495","author":"Luo","year":"2025","journal-title":"Food Chem."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Rypar, T., Molcanova, L., Valkova, B., Hromadkova, E., Bueschl, C., Seidl, B., Smejkal, K., and Schuhmacher, R. (2025). Targeted Isolation of Prenylated Flavonoids from Paulownia Tomentosa Fruit Extracts via AI-Guided Workflow Integrating LC-UV-HRMS\/MS. Metabolites, 15.","DOI":"10.3390\/metabo15090616"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"e70086","DOI":"10.1111\/tpj.70086","article-title":"Computational Metabolomics Reveals Overlooked Chemodiversity of Alkaloid Scaffolds in Piper Fimbriulatum","volume":"121","author":"Damiani","year":"2025","journal-title":"Plant J."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"4359","DOI":"10.1021\/acs.analchem.3c02958","article-title":"Metabolomics and Molecular Networking-Guided Screening of Bacillus -Derived Bioactive Compounds Against a Highly Lethal Vibrio Species","volume":"96","author":"Xia","year":"2024","journal-title":"Anal. Chem."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"106070","DOI":"10.1016\/j.fitote.2024.106070","article-title":"Biological and Metabolomics-Guided Isolation of Tetrahydrofurofuran Lignan from Croton Spp. with Antiproliferative Activity against Human Melanoma Cell Line","volume":"177","author":"Garcia","year":"2024","journal-title":"Fitoterapia"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"btae584","DOI":"10.1093\/bioinformatics\/btae584","article-title":"Combined LC-MS\/MS Feature Grouping, Statistical Prioritization, and Interactive Networking in MsFeaST","volume":"40","author":"Mildau","year":"2024","journal-title":"Bioinformatics"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"8118","DOI":"10.1038\/s41467-025-63536-6","article-title":"Knowledge and Data-Driven Two-Layer Networking for Accurate Metabolite Annotation in Untargeted Metabolomics","volume":"16","author":"Zhang","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Lovri\u0107, M., Wang, T., Staffe, M.R., \u0160uni\u0107, I., \u010casni, K., Lasky-Su, J., Chawes, B., and Rasmussen, M.A. (2024). A Chemical Structure and Machine Learning Approach to Assess the Potential Bioactivity of Endogenous Metabolites and Their Association with Early Childhood Systemic Inflammation. Metabolites, 14.","DOI":"10.3390\/metabo14050278"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1021\/acs.jnatprod.4c01140","article-title":"Metabologenomics-Driven Discovery of Nocardimicins from a Psychrophilic Nocardia Sp. Strain","volume":"88","author":"Xu","year":"2025","journal-title":"J. Nat. Prod."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"18075","DOI":"10.1021\/acs.analchem.5c02213","article-title":"NMR-Metabologenomics Coupled with Activity-Picking Inspires Discovery of Samsumycins: A Fast-Mining Strategy for Novel Antibiotics","volume":"97","author":"Bao","year":"2025","journal-title":"Anal. Chem."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1039\/D4SC04174G","article-title":"A Metabologenomics Strategy for Rapid Discovery of Polyketides Derived from Modular Polyketide Synthases","volume":"16","author":"Liu","year":"2025","journal-title":"Chem. Sci."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Cheng, Z., He, B.-B., Lei, K., Gao, Y., Shi, Y., Zhong, Z., Liu, H., Liu, R., Zhang, H., and Wu, S. (2023). Biosynthesis- and Metabolomics-Guided Discovery of Antimicrobial Cyclopeptides against Drug-Resistant Clinical Isolates. bioRxiv, preprint.","DOI":"10.1101\/2023.10.26.563470"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"8089","DOI":"10.1039\/D4SC00825A","article-title":"Discovery of a Lagriamide Polyketide by Integrated Genome Mining, Isotopic Labeling, and Untargeted Metabolomics","volume":"15","author":"Fergusson","year":"2024","journal-title":"Chem. Sci."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"661","DOI":"10.62411\/faith.3048-3719-304","article-title":"SysFungiNet: A Multi-Omics Data Fusion Framework with Explainable AI for Bioactive Prioritization","volume":"2","author":"Oluwagbemi","year":"2026","journal-title":"J. Futur. Artif. Intell. Technol."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Tay, D.W.P., Koh, W., Ang, S.J., Wong, Z.M., Lim, Y.W., Heng, E., Yeo, N.Z.X., Adaikkappan, K., Wong, F.T., and Lim, Y.H. (2025). Accelerating Natural Product Discovery with Linked MS-Genomics and Language\/Transformer-Based Models. bioRxiv, preprint.","DOI":"10.1101\/2025.01.20.633932"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/s41586-025-09969-x","article-title":"Language Model-Guided Anticipation and Discovery of Mammalian Metabolites","volume":"651","author":"Qiang","year":"2026","journal-title":"Nature"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"btaf354","DOI":"10.1093\/bioinformatics\/btaf354","article-title":"JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data","volume":"41","author":"Kalia","year":"2025","journal-title":"Bioinformatics"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Rosa, D., Elya, B., Hanafi, M., Khatib, A., Budiarto, E., Nur, S., and Surya, M.I. (2025). Investigation of Alpha-Glucosidase Inhibition Activity of Artabotrys Sumatranus Leaf Extract Using Metabolomics, Machine Learning and Molecular Docking Analysis. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0313592"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"8317","DOI":"10.1021\/acs.orglett.4c03024","article-title":"Metabologenomics-Inspired Discovery and Combinatorial Biosynthesis-Based Diversification of Fungal O-Glycosylated Depsides","volume":"26","author":"Yang","year":"2024","journal-title":"Org. Lett."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Oluwabusola, E.T., Jackson, S.A., Brunati, C., Gackstatter, S., Vedder, H., Iorio, M., Chawande, G., Margassery, L.M., Nguyen, G.-S., and Clarke, D.J. (2025). Integrated Omics-Based Discovery of Bioactive Halogenated Metabolites from the Deep-Sea Streptomyces Sp. B188M101. Mar. Drugs, 23.","DOI":"10.3390\/md23090362"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Liu, T., Gui, X., Zhang, G., Luo, L., and Zhao, J. (2024). Streptomyces-Fungus Co-Culture Enhances the Production of Borrelidin and Analogs: A Genomic and Metabolomic Approach. Mar. Drugs, 22.","DOI":"10.3390\/md22070302"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.1021\/acschembio.5c00346","article-title":"Characterization of Variation in Natural Product Production Under Chemical Elicitation Using Parallel Stable Isotope Labeling","volume":"20","author":"Zaroubi","year":"2025","journal-title":"ACS Chem. Biol."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"116797","DOI":"10.1016\/j.ejmech.2024.116797","article-title":"Discovery of AMPs from Random Peptides via Deep Learning-Based Model and Biological Activity Validation","volume":"277","author":"Du","year":"2024","journal-title":"Eur. J. Med. Chem."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s00210-024-03276-4","article-title":"In Silico Method and Bioactivity Evaluation to Discover Novel Antimicrobial Agents Targeting FtsZ Protein: Machine Learning, Virtual Screening and Antibacterial Mechanism Study","volume":"398","author":"Wang","year":"2025","journal-title":"Naunyn. Schmiedebergs. Arch. Pharmacol."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1128\/msystems.00325-24","article-title":"Antibiotic Discovery with Artificial Intelligence for the Treatment of Acinetobacter Baumannii Infections","volume":"9","author":"Boulaamane","year":"2024","journal-title":"mSystems"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"9357","DOI":"10.1021\/acsomega.3c08676","article-title":"Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models","volume":"9","author":"Tsai","year":"2024","journal-title":"ACS Omega"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"7171","DOI":"10.1126\/sciadv.adp7171","article-title":"Artificial Intelligence Using a Latent Diffusion Model Enables the Generation of Diverse and Potent Antimicrobial Peptides","volume":"11","author":"Wang","year":"2025","journal-title":"Sci. Adv."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.1038\/s41467-026-69306-2","article-title":"Discovery of Antimicrobial Peptides Targeting Acinetobacter Baumannii via a Pre-Trained and Fine-Tuned Few-Shot Learning-Based Pipeline","volume":"17","author":"Huang","year":"2026","journal-title":"Nat. Commun."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"bbaf343","DOI":"10.1093\/bib\/bbaf343","article-title":"AI-Guided Discovery and Optimization of Antimicrobial Peptides through Species-Aware Language Model","volume":"26","author":"Bae","year":"2025","journal-title":"Brief. Bioinform."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.cell.2020.01.021","article-title":"A Deep Learning Approach to Antibiotic Discovery","volume":"180","author":"Stokes","year":"2020","journal-title":"Cell"},{"key":"ref_129","first-page":"238","article-title":"Predicting Novel Antibacterial Agents","volume":"19","author":"Crunkhorn","year":"2020","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_130","first-page":"238","article-title":"Inhibiting Peptidoglycan Remodelling","volume":"19","author":"Crunkhorn","year":"2020","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"17854","DOI":"10.1039\/D1NR04178A","article-title":"Towards Machine Learning Discovery of Dual Antibacterial Drug\u2013Nanoparticle Systems","volume":"13","year":"2021","journal-title":"Nanoscale"}],"container-title":["Antibiotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6382\/15\/5\/478\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T04:12:02Z","timestamp":1779250322000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6382\/15\/5\/478"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":131,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["antibiotics15050478"],"URL":"https:\/\/doi.org\/10.3390\/antibiotics15050478","relation":{},"ISSN":["2079-6382"],"issn-type":[{"value":"2079-6382","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]}}}