{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T01:54:09Z","timestamp":1790906049086,"version":"4.1.0"},"reference-count":48,"publisher":"American Chemical Society (ACS)","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003451","name":"Euskal Herriko Unibertsitatea","doi-asserted-by":"publisher","award":["NA"],"award-info":[{"award-number":["NA"]}],"id":[{"id":"10.13039\/501100003451","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011033","name":"Agencia Estatal de Investigaci?n","doi-asserted-by":"publisher","award":["PID2020-116495RB-I00"],"award-info":[{"award-number":["PID2020-116495RB-I00"]}],"id":[{"id":"10.13039\/501100011033","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Bundesministerium f?r Bildung und Forschung","award":["01DO2200"],"award-info":[{"award-number":["01DO2200"]}]},{"name":"Bundesministerium f?r Bildung und Forschung","award":["01KI2401"],"award-info":[{"award-number":["01KI2401"]}]},{"name":"Bundesministerium f?r Bildung und Forschung","award":["01KI2404A"],"award-info":[{"award-number":["01KI2404A"]}]},{"name":"German Research Council","award":["544004729"],"award-info":[{"award-number":["544004729"]}]},{"name":"Ministerio de Ciencia e Innovaci?n","award":["PID2020-116495RB-I00"],"award-info":[{"award-number":["PID2020-116495RB-I00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Chemical stressors in environmental and clinical settings can alter conjugative plasmid transfer, which is a key process driving antibiotic resistance dissemination. However, predicting these effects across chemically diverse compounds is challenging. Here, we compiled and curated data on the effect of chemical stressors on RP4 plasmid transfer, extracting compound identity, concentration, conjugation frequency, and experimental metadata from 73 in vitro studies. Chemicals were characterized by functional use categories and molecular descriptors, including physicochemical properties, MACCS keys, Morgan fingerprints, and extended descriptors from the OCHEM platform (AlvaDesc, Dragon 7, and 3D-shape descriptors). An exhaustive evaluation of 1022 feature set combinations across 3066 models showed that chemical concentration alone provides no meaningful predictive signal (Q2ext &amp;lt;0), whereas integrating Morgan fingerprints substantially improves predictive performance. The best-performing model combined concentration, Morgan fingerprints, and experimental conditions using extreme gradient boosting (XGB), achieving Q2ext = 0.373 on a held-out compound-aware test set and a median Q2ext of 0.319 within the defined applicability domain across 20 independent splits. SHAP analysis revealed that Morgan fingerprints account for 69% of total predictive weight, with quaternary ammonium compounds and long-chain alkyl structures associated with inhibition of conjugation and sulfonamide and amine-containing compounds associated with promotion. These results demonstrate that chemical structural features provide critical information for predicting the chemical modulation of conjugative plasmid transfer. This work highlights that combining chemically informed descriptors with machine learning (ML) anticipates chemical contributions to antibiotic resistance dissemination, providing a scalable framework for the prioritization and risk assessment of chemicals.<\/jats:p>","DOI":"10.1021\/acs.est.6c02828","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T01:01:38Z","timestamp":1790902898000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting\nChemical\nStressor Effects on Conjugative\nPlasmid Transfer Rates Using Machine Learning"],"prefix":"10.1021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0245-1804","authenticated-orcid":true,"given":"Ana","family":"Rey-Sogo","sequence":"first","affiliation":[{"name":"Euskal Herriko Unibertsitatea (EHU) , , , ,","place":["Leioa, Bizkaia, Spain, 48940"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6048-6984","authenticated-orcid":true,"given":"David","family":"Kneis","sequence":"additional","affiliation":[{"name":"Technische Universita\u0308t Dresden , , , ,","place":["Dresden, Saxony, Germany, 01217"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan Xavier","family":"Elena","sequence":"additional","affiliation":[{"name":"Technische Universita\u0308t Dresden , , , ,","place":["Dresden, Saxony, Germany, 01217"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4169-6548","authenticated-orcid":true,"given":"Uli","family":"Klu\u0308mper","sequence":"additional","affiliation":[{"name":"Technische Universita\u0308t Dresden , , , ,","place":["Dresden, Saxony, Germany, 01217"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Itziar","family":"Alkorta","sequence":"additional","affiliation":[{"name":"Euskal Herriko Unibertsitatea (EHU) , , , ,","place":["Leioa, Bizkaia, Spain, 48940"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,10,2]]},"reference":[{"key":"2026100121013017900_cit1","unstructured":"World Health Organization.\n          .  Global Antibiotic Resistance\nSurveillance ReportWorld Health Organization: Geneva; 2025https:\/\/www.who.int\/publications\/i\/item\/9789240116337."},{"issue":"2","key":"2026100121013017900_cit2","doi-asserted-by":"publisher","DOI":"10.1128\/microbiolspec.ARBA-0009-2017","article-title":"Antimicrobial\nResistance: A One Health Perspective","volume":"6","author":"McEwen","year":"2018","journal-title":"Microbiol.\nSpectr."},{"issue":"14","key":"2026100121013017900_cit3","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkaf652","article-title":"The Extended Mobility of Plasmids","volume":"53","author":"Garcilla\u0301n-Barcia","year":"2025","journal-title":"Nucleic Acids Res."},{"issue":"3","key":"2026100121013017900_cit4","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1093\/jac\/dkab450","article-title":"Antimicrobial-Induced\nHorizontal\nTransfer of Antimicrobial Resistance Genes in Bacteria: A Mini-Review","volume":"77","author":"Liu","year":"2022","journal-title":"J. Antimicrob. Chemother."},{"key":"2026100121013017900_cit5","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1038\/ismej.2014.191","article-title":"Broad Host Range\nPlasmids Can Invade an Unexpectedly Diverse Fraction of a Soil Bacterial\nCommunity","volume":"9","author":"Klu\u0308mper","year":"2015","journal-title":"ISME J."},{"key":"2026100121013017900_cit6","unstructured":"Environmental Protection Agency (EPA).\n          .  Innovative Monitoring to Prioritise Contaminants\nof Emerging\nConcern for Ireland: ImpactWexford, Ireland; 2024."},{"issue":"3","key":"2026100121013017900_cit7","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1002\/etc.5498","article-title":"Does Environmental\nExposure to Pharmaceutical\nand Personal Care Product Residues Result in the Selection of Antimicrobial-Resistant\nMicroorganisms, and Is This Important in Terms of Human Health Outcomes?","volume":"43","author":"Stanton","year":"2024","journal-title":"Environ. Toxicol. 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Technol."},{"issue":"7","key":"2026100121013017900_cit9","doi-asserted-by":"publisher","first-page":"2117","DOI":"10.1038\/s41396-021-00909-x","article-title":"Nonnutritive\nSweeteners Can Promote the Dissemination of Antibiotic Resistance\nthrough Conjugative Gene Transfer","volume":"15","author":"Yu","year":"2021","journal-title":"ISME J."},{"issue":"1","key":"2026100121013017900_cit10","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1038\/ismej.2016.98","article-title":"Metal Stressors Consistently Modulate\nBacterial Conjugal Plasmid Uptake Potential in a Phylogenetically\nConserved Manner","volume":"11","author":"Klu\u0308mper","year":"2017","journal-title":"ISME J."},{"issue":"9","key":"2026100121013017900_cit11","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1038\/s41396-021-00945-7","article-title":"Non-Antibiotic\nPharmaceuticals Promote\nthe Transmission of Multidrug Resistance Plasmids through Intra- and\nIntergenera Conjugation","volume":"15","author":"Wang","year":"2021","journal-title":"ISME J."},{"key":"2026100121013017900_cit12","doi-asserted-by":"crossref","unstructured":"Rodriguez-Grande, J.; Ortiz, Y.; Garcillan-Barcia, M. P.; de la Cruz, F.; Fernandez-Lopez, R.\n          Fundamental Parameters\nGoverning the Transmission of Conjugative Plasmids\n          bioRxiv\n          2023\n          10.1101\/2023.07.11.548640.","DOI":"10.1101\/2023.07.11.548640"},{"key":"2026100121013017900_cit13","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2023.163870","article-title":"Plasmid-Mediated Antibiotic\nResistance Gene Transfer under Environmental\nStresses: Insights from Laboratory-Based Studies","volume":"887","author":"Li","year":"2023","journal-title":"Sci. Total Environ."},{"issue":"8","key":"2026100121013017900_cit14","doi-asserted-by":"publisher","DOI":"10.3390\/microorganisms8081211","article-title":"Correlation between Exogenous Compounds\nand the Horizontal Transfer\nof Plasmid-Borne Antibiotic Resistance Genes","volume":"8","author":"Liu","year":"2020","journal-title":"Microorganisms"},{"issue":"19","key":"2026100121013017900_cit15","doi-asserted-by":"publisher","DOI":"10.3390\/molecules27196579","article-title":"Factors Influencing the Bioavailability\nof Organic\nMolecules to Bacterial Cells\u2500A Mini-Review","volume":"27","author":"Smu\u0142ek","year":"2022","journal-title":"Molecules"},{"issue":"14","key":"2026100121013017900_cit16","doi-asserted-by":"publisher","DOI":"10.3390\/ijms241411488","article-title":"Recent Advances in Machine-Learning-Based\nChemoinformatics:\nA Comprehensive Review","volume":"24","author":"Niazi","year":"2023","journal-title":"Int. J. Mol. 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Model."},{"key":"2026100121013017900_cit26","unstructured":"Lundberg, S. M.; Lee, S. I. In  A Unified\nApproach\nto Interpreting Model Predictions; Advances in Neural Information\nProcessing Systems.302 ACM, 2017."},{"issue":"9","key":"2026100121013017900_cit27","doi-asserted-by":"publisher","first-page":"3633","DOI":"10.1111\/1462-2920.15174","article-title":"Benefits at the Nanoscale:\nA Review of Nanoparticle-Enabled Processes\nFavouring Microbial Growth and Functionality","volume":"22","author":"Mansor","year":"2020","journal-title":"Environ. Microbiol."},{"issue":"3","key":"2026100121013017900_cit28","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1111\/j.1574-6941.2006.00230.x","article-title":"Effects of Stress and\nOther Environmental Factors on Horizontal Plasmid\nTransfer Assessed by Direct Quantification of Discrete Transfer Events","volume":"59","author":"Johnsen","year":"2007","journal-title":"FEMS Microbiol. Ecol."},{"key":"2026100121013017900_cit29","doi-asserted-by":"crossref","unstructured":"Chen, T.; Guestrin, C. In  XGBoost:\nA Scalable\nTree Boosting System; Proceedings of the ACM SIGKDD International\nConference on Knowledge Discovery and Data Mining ACM, 2016; pp 785\u201379410.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"issue":"5","key":"2026100121013017900_cit30","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy\nFunction Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"issue":"1","key":"2026100121013017900_cit31","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. 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Intell. Lab.\nSyst."},{"key":"2026100121013017900_cit36","doi-asserted-by":"publisher","DOI":"10.1787\/9789264085442-en","article-title":"Guidance Document\non the Validation of (Quantitative) Structure-Activity Relationship\n[(Q)SAR] Models","volume-title":"OECD Series on Testing\nand Assessment","author":"OECD.","year":"2007"},{"issue":"17","key":"2026100121013017900_cit37","doi-asserted-by":"publisher","DOI":"10.1128\/AEM.00948-20","article-title":"The Conjugation Window in an Escherichia coli K-12 Strain with an IncFII Plasmid","volume":"86","author":"Headd","year":"2020","journal-title":"Appl. Environ. Microbiol."},{"issue":"27","key":"2026100121013017900_cit38","doi-asserted-by":"publisher","first-page":"28352","DOI":"10.1007\/s11356-019-05673-2","article-title":"The Impact and Mechanism of Quaternary Ammonium Compounds on the\nTransmission of Antibiotic Resistance Genes","volume":"26","author":"Han","year":"2019","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"2026100121013017900_cit39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhazmat.2025.137932","article-title":"Sulfonamide\nMetabolites Enhance Resistance Transmission via Conjugative Transfer\nPathways","volume":"491","author":"Zhang","year":"2025","journal-title":"J. Hazard. Mater."},{"issue":"10","key":"2026100121013017900_cit40","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adk6669","article-title":"Transformers\nEnable Accurate Prediction of Acute and Chronic Chemical Toxicity\nin Aquatic Organisms","volume":"10","author":"Gustavsson","year":"2024","journal-title":"Sci. Adv."},{"issue":"2","key":"2026100121013017900_cit41","doi-asserted-by":"publisher","DOI":"10.1128\/mbio.03431-25","article-title":"Confidence-Based Prediction of Antibiotic\nResistance at the Patient Level","volume":"17","author":"Inda-Di\u0301az","year":"2026","journal-title":"mBio"},{"issue":"1","key":"2026100121013017900_cit42","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-57825-3","article-title":"Genetic Compatibility and Ecological\nConnectivity Drive the Dissemination of Antibiotic Resistance Genes","volume":"16","author":"Lund","year":"2025","journal-title":"Nat. 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Drug Discovery"},{"issue":"24","key":"2026100121013017900_cit46","doi-asserted-by":"publisher","DOI":"10.3390\/molecules24244537","article-title":"Nano-(Q)SAR\nfor Cytotoxicity Prediction\nof Engineered Nanomaterials","volume":"24","author":"Buglak","year":"2019","journal-title":"Molecules"},{"issue":"11","key":"2026100121013017900_cit47","doi-asserted-by":"publisher","first-page":"2989","DOI":"10.1039\/D3EN00598D","article-title":"Evaluating\nMetal Oxide Nanoparticle (MeOx NP) Toxicity with Different\nTypes of Nano Descriptors Mainly Focusing on Simple Periodic Table-Based\nDescriptors: A Mini-Review","volume":"10","author":"Roy","year":"2023","journal-title":"Environ. 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