{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T16:07:34Z","timestamp":1790870854073,"version":"4.1.0"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016175","name":"Chulabhorn Royal Academy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016175","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016175","name":"Chulabhorn Royal Academy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016175","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016175","name":"Chulabhorn Royal Academy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016175","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>\n                    Antimicrobial resistance (AMR) is a critical global health problem that has become increasingly alarming in recent years. The discovery of new antibiotics is one approach for alleviating AMR, however, screening for novel drugs is time consuming and expensive. To accelerate antibiotic discovery, the integration of machine learning algorithms with Quantitative Structure\u2013Activity Relationship (QSAR) calculations could provide a rapid solution. Thus, this study combines a QSAR model and machine learning algorithms to predict antibacterial activities of potential novel drugs based on chemical information. Information on compounds that are reportedly active and inactive against bacteria was downloaded from the PubChem database and manually curated to create positive and negative datasets. The decision tree (DT), support vector machine (SVM), and na\u00efve Bayesian (NB) algorithms were employed to predict the antibacterial activities of chemical compounds from their Simplified Molecular Input Line Entry System (SMILES) information. The models were then evaluated quantitatively and tuned. DT and SVM exhibited comparable predictive performance and outperformed the NB model, achieving accuracy, precision, sensitivity, and AUC-ROC values exceeding 0.90. DT was chosen for further analysis because of its simplicity and effectiveness. This revealed that descriptors relating to the electrotopology and \u03b2-lactam structures of compounds were the top contributors to model predictability. The model was then further tested against different classes of antibiotics and achieved high accuracy in all classes. The model is freely available as a web application at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/antibacterial-predictor-model-ocogzqyibervrqb7trvfev.streamlit.app\/\" xlink:type=\"simple\">https:\/\/antibacterial-predictor-model-ocogzqyibervrqb7trvfev.streamlit.app\/<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1371\/journal.pdig.0001593","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T17:21:16Z","timestamp":1786641676000},"page":"e0001593","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning-based quantitative structure\u2013activity relationship model for antibiotic prediction and discovery"],"prefix":"10.1371","volume":"5","author":[{"given":"Jiratchaya","family":"Nakbang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chonthicha","family":"Arbsuwan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Santitham","family":"Prom-on","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1088-9451","authenticated-orcid":true,"given":"Peerut","family":"Chienwichai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"issue":"10459","key":"pdig.0001593.ref001","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/S0140-6736(24)01867-1","article-title":"Global burden of bacterial antimicrobial resistance 1990-2021: a systematic analysis with forecasts to 2050","volume":"404","author":"GBD 2021 Antimicrobial Resistance Collaborators","year":"2024","journal-title":"Lancet"},{"key":"pdig.0001593.ref002","doi-asserted-by":"crossref","first-page":"106103","DOI":"10.1016\/j.ejps.2021.106103","article-title":"Antimicrobial resistance: Prevalence, economic burden, mechanisms of resistance and strategies to overcome","volume":"170","author":"T Pulingam","year":"2022","journal-title":"Eur J Pharm Sci"},{"key":"pdig.0001593.ref003","doi-asserted-by":"crossref","first-page":"100081","DOI":"10.1016\/j.glmedi.2024.100081","article-title":"Antimicrobial resistance: Impacts, challenges, and future prospects","volume":"2","author":"SK Ahmed","year":"2024","journal-title":"J Med Sur Public Health"},{"issue":"5","key":"pdig.0001593.ref004","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1016\/j.mcna.2018.04.003","article-title":"Antimicrobial Stewardship Programs: Principles and Practice","volume":"102","author":"CB Cunha","year":"2018","journal-title":"Med Clin North Am"},{"issue":"5","key":"pdig.0001593.ref005","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.mcna.2018.04.005","article-title":"Antimicrobial Resistance: An Antimicrobial\/Diagnostic Stewardship and Infection Prevention Approach","volume":"102","author":"EJ Septimus","year":"2018","journal-title":"Med Clin North Am"},{"issue":"3","key":"pdig.0001593.ref006","doi-asserted-by":"crossref","DOI":"10.1016\/S1473-3099(17)30485-1","article-title":"Surveillance for control of antimicrobial resistance","volume":"18","author":"E Tacconelli","year":"2018","journal-title":"Lancet Infect Dis"},{"issue":"7","key":"pdig.0001593.ref007","doi-asserted-by":"crossref","DOI":"10.1002\/vetr.10","article-title":"Characterisation and mapping of the surveillance system for antimicrobial resistance and antimicrobial use in the United Kingdom","volume":"188","author":"H Bennani","year":"2021","journal-title":"Vet Rec"},{"issue":"2","key":"pdig.0001593.ref008","doi-asserted-by":"crossref","first-page":"87","DOI":"10.7883\/yoken.JJID.2020.499","article-title":"Japan Nosocomial Infections Surveillance (JANIS): Current Status, International Collaboration, and Future Directions for a Comprehensive Antimicrobial Resistance Surveillance System","volume":"74","author":"T Kajihara","year":"2021","journal-title":"Jpn J Infect Dis"},{"issue":"3","key":"pdig.0001593.ref009","doi-asserted-by":"crossref","first-page":"432","DOI":"10.4103\/0971-5916.261122","article-title":"Erratum: Antimicrobial susceptibility profile & resistance mechanisms of Global Antimicrobial Resistance Surveillance System (GLASS) priority pathogens from India","volume":"149","year":"2019","journal-title":"Indian J Med Res"},{"issue":"9","key":"pdig.0001593.ref010","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.3390\/microorganisms12091920","article-title":"An Overview of the Recent Advances in Antimicrobial Resistance","volume":"12","author":"M Oliveira","year":"2024","journal-title":"Microorganisms"},{"issue":"12","key":"pdig.0001593.ref011","doi-asserted-by":"crossref","DOI":"10.1128\/spectrum.01854-24","article-title":"Broad-spectrum antimicrobial activities of a food fermentate of Aspergillus oryzae","volume":"12","author":"D Choi","year":"2024","journal-title":"Microbiol Spectr"},{"issue":"15","key":"pdig.0001593.ref012","doi-asserted-by":"crossref","first-page":"12044","DOI":"10.3390\/ijms241512044","article-title":"New Evidence for Artemisia absinthium as an Alternative to Classical Antibiotics: Chemical Analysis of Phenolic Compounds, Screening for Antimicrobial Activity","volume":"24","author":"Z Liu","year":"2023","journal-title":"Int J Mol Sci"},{"key":"pdig.0001593.ref013","doi-asserted-by":"crossref","first-page":"107927","DOI":"10.1016\/j.compbiomed.2024.107927","article-title":"The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials","volume":"169","author":"SO Oselusi","year":"2024","journal-title":"Comput Biol Med"},{"issue":"4","key":"pdig.0001593.ref014","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1080\/10799893.2021.1988971","article-title":"DFT based QSAR study on quinolone-triazole derivatives as antibacterial agents","volume":"42","author":"N Ghasedi","year":"2022","journal-title":"J Recept Signal Transduct Res"},{"issue":"11","key":"pdig.0001593.ref015","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0259348","article-title":"In silico repurposing of a Novobiocin derivative for activity against latency associated Mycobacterium tuberculosis drug target nicotinate-nucleotide adenylyl transferase (Rv2421c)","volume":"16","author":"R Cloete","year":"2021","journal-title":"PLoS One"},{"issue":"2","key":"pdig.0001593.ref016","doi-asserted-by":"crossref","first-page":"147","DOI":"10.3831\/KPI.2023.26.2.147","article-title":"In Silico Studies of Indole Derivatives as Antibacterial Agents","volume":"26","author":"M Shah","year":"2023","journal-title":"J Pharmacopuncture"},{"issue":"12","key":"pdig.0001593.ref017","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e32397","article-title":"A QSPR analysis and curvilinear regression models for various degree-based topological indices: Quinolone antibiotics","volume":"10","author":"B Kirana","year":"2024","journal-title":"Heliyon"},{"issue":"6","key":"pdig.0001593.ref018","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","article-title":"Applications of machine learning in drug discovery and development","volume":"18","author":"J Vamathevan","year":"2019","journal-title":"Nat Rev Drug Discov"},{"issue":"22","key":"pdig.0001593.ref019","doi-asserted-by":"crossref","first-page":"5277","DOI":"10.3390\/molecules25225277","article-title":"Machine Learning Methods in Drug Discovery","volume":"25","author":"L Patel","year":"2020","journal-title":"Molecules"},{"issue":"3","key":"pdig.0001593.ref020","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1007\/s11030-021-10217-3","article-title":"Artificial intelligence to deep learning: machine intelligence approach for drug discovery","volume":"25","author":"R Gupta","year":"2021","journal-title":"Mol Divers"},{"issue":"3","key":"pdig.0001593.ref021","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1007\/s11030-021-10238-y","article-title":"Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents","volume":"26","author":"MA Wani","year":"2022","journal-title":"Mol Divers"},{"issue":"4","key":"pdig.0001593.ref022","doi-asserted-by":"crossref","first-page":"2256","DOI":"10.3390\/ijms25042256","article-title":"Design and Synthesis of Pleuromutilin Derivatives as Antibacterial Agents Using Quantitative Structure-Activity Relationship Model","volume":"25","author":"J Zhang","year":"2024","journal-title":"Int J Mol Sci"},{"issue":"6","key":"pdig.0001593.ref023","doi-asserted-by":"crossref","DOI":"10.1128\/msystems.00325-24","article-title":"Antibiotic discovery with artificial intelligence for the treatment of Acinetobacter baumannii infections","volume":"9","author":"Y Boulaamane","year":"2024","journal-title":"mSystems"},{"key":"pdig.0001593.ref024","article-title":"PubChem 2023 update","volume":"51","author":"S Kim","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"pdig.0001593.ref025","author":"G Landrum","year":"2022"},{"issue":"1","key":"pdig.0001593.ref026","doi-asserted-by":"crossref","first-page":"17","DOI":"10.2174\/1570163817666200316104404","article-title":"An analysis of QSAR research based on machine learning concepts","volume":"18","author":"MR Keyvanpour","year":"2021","journal-title":"Curr Drug Discov Technol"},{"key":"pdig.0001593.ref027","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"F Pedregosa","year":"2011","journal-title":"J Machine Learning Res"},{"key":"pdig.0001593.ref028","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"SM Lundberg","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"pdig.0001593.ref029","first-page":"90","article-title":"Antibiotics: classification and mechanisms of action with emphasis on molecular perspectives","volume":"4","author":"E Etebu","year":"2016","journal-title":"Int J Appl Microbiol Biotechnol Res"},{"key":"pdig.0001593.ref030","author":"Streamlit Inc","year":"2026"},{"key":"pdig.0001593.ref031","doi-asserted-by":"crossref","first-page":"107681","DOI":"10.1016\/j.compbiomed.2023.107681","article-title":"PmxPred: A data-driven approach for the identification of active polymyxin analogues against gram-negative bacteria","volume":"168","author":"X Wang","year":"2024","journal-title":"Comput Biol Med"},{"issue":"1","key":"pdig.0001593.ref032","doi-asserted-by":"crossref","first-page":"4641","DOI":"10.1038\/s41598-024-55418-6","article-title":"Molecular guidelines for promising antimicrobial agents","volume":"14","author":"M Rzycki","year":"2024","journal-title":"Sci Rep"},{"issue":"8","key":"pdig.0001593.ref033","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1021\/acsinfecdis.8b00036","article-title":"Molecular Properties That Define the Activities of Antibiotics in Escherichia coli and Pseudomonas aeruginosa","volume":"4","author":"CJ Cooper","year":"2018","journal-title":"ACS Infect Dis"},{"issue":"6","key":"pdig.0001593.ref034","doi-asserted-by":"crossref","first-page":"2120","DOI":"10.1021\/ci030019w","article-title":"Modeling drug albumin binding affinity with e-state topological structure representation","volume":"43","author":"LM Hall","year":"2003","journal-title":"J Chem Inf Comput Sci"},{"key":"pdig.0001593.ref035","doi-asserted-by":"crossref","first-page":"1305294","DOI":"10.3389\/fphar.2023.1305294","article-title":"Antibiotic action and resistance: updated review of mechanisms, spread, influencing factors, and alternative approaches for combating resistance","volume":"14","author":"EM Halawa","year":"2024","journal-title":"Front Pharmacol"},{"key":"pdig.0001593.ref036","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1007\/PL00000825","article-title":"Towards new beta-lactam antibiotics","volume":"58","author":"I Andersson","year":"2001","journal-title":"Cell Mol Life Sci"},{"key":"pdig.0001593.ref037","doi-asserted-by":"crossref","first-page":"112829","DOI":"10.1016\/j.ejmech.2020.112829","article-title":"\u03b2-lactam antibiotics: An overview from a medicinal chemistry perspective","volume":"208","author":"LM Lima","year":"2020","journal-title":"Eur J Med Chem"},{"issue":"9","key":"pdig.0001593.ref038","doi-asserted-by":"crossref","first-page":"4207","DOI":"10.1021\/jo400236f","article-title":"Overcoming resistance to \u03b2-lactam antibiotics","volume":"78","author":"RJ Worthington","year":"2013","journal-title":"J Org Chem"},{"issue":"1","key":"pdig.0001593.ref039","doi-asserted-by":"crossref","first-page":"2243416","DOI":"10.1080\/21655979.2023.2243416","article-title":"A data-driven machine learning approach for discovering potent LasR inhibitors","volume":"14","author":"CMM Koh","year":"2023","journal-title":"Bioengineered"},{"issue":"9","key":"pdig.0001593.ref040","doi-asserted-by":"crossref","first-page":"8180","DOI":"10.3390\/ijms24098180","article-title":"Using Machine Learning and Molecular Docking to Leverage Urease Inhibition Data for Virtual Screening","volume":"24","author":"N Aniceto","year":"2023","journal-title":"Int J Mol Sci"},{"issue":"8","key":"pdig.0001593.ref041","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1002\/jcc.21148","article-title":"Prediction of antibacterial compounds by machine learning approaches","volume":"30","author":"X-G Yang","year":"2009","journal-title":"J Comput Chem"},{"issue":"3","key":"pdig.0001593.ref042","doi-asserted-by":"crossref","first-page":"4848","DOI":"10.3390\/molecules20034848","article-title":"QSAR-assisted virtual screening of lead-like molecules from marine and microbial natural sources for antitumor and antibiotic drug discovery","volume":"20","author":"F Pereira","year":"2015","journal-title":"Molecules"},{"key":"pdig.0001593.ref043","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkad1004","article-title":"The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods","volume":"52","author":"B Zdrazil","year":"2024","journal-title":"Nucleic Acids Res"},{"issue":"9","key":"pdig.0001593.ref044","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1093\/jac\/dky208","article-title":"Revitalizing the drug pipeline: AntibioticDB, an open access database to aid antibacterial research and development","volume":"73","author":"LJ Farrell","year":"2018","journal-title":"J Antimicrob Chemother"}],"container-title":["PLOS Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0001593","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T17:21:24Z","timestamp":1786641684000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0001593"}},"subtitle":[],"editor":[{"given":"Dukyong","family":"Yoon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,8,13]]},"references-count":44,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8,13]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pdig.0001593","relation":{},"ISSN":["2767-3170"],"issn-type":[{"value":"2767-3170","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,13]]}}}