{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:50:10Z","timestamp":1781016610086,"version":"3.54.1"},"reference-count":60,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2022YFC2703105"],"award-info":[{"award-number":["2022YFC2703105"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2021YFF1201303"],"award-info":[{"award-number":["2021YFF1201303"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAMS Innovation Fund for Medical Sciences","award":["2021-I2M-1-056"],"award-info":[{"award-number":["2021-I2M-1-056"]}]},{"name":"IMICAMS","award":["2024YT03"],"award-info":[{"award-number":["2024YT03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat the spread of this global health burden more difficult. To accelerate anti-tuberculosis drug discovery, repurposing clinically approved or investigational drugs for the treatment of tuberculosis by computational methods has become an attractive strategy. In this study, we developed a virtual screening workflow that combines multiple machine learning and deep learning models, and 11\u00a0576 compounds extracted from the DrugBank database were screened against Mtb. Our screening method produced satisfactory predictions on three data-splitting settings, with the top predicted bioactive compounds all known antibacterial or anti-TB drugs. To further identify and evaluate drugs with repurposing potential in TB therapy, 15 screened potential compounds were selected for subsequent computational and experimental evaluations, out of which aldoxorubicin and quarfloxin showed potent inhibition of Mtb strain H37Rv, with minimal inhibitory concentrations of 4.16 and 20.67\u00a0\u03bcM\/mL, respectively. More inspiringly, these two compounds also showed antibacterial activity against multidrug-resistant TB isolates and exhibited strong antimicrobial activity against Mtb. Furthermore, molecular docking, molecular dynamics simulation, and the surface plasmon resonance experiments validated the direct binding of the two compounds to Mtb DNA gyrase. In summary, our effective comprehensive virtual screening workflow successfully repurposed two novel drugs (aldoxorubicin and quarfloxin) as promising anti-Mtb candidates. The verification results provide useful information for the further development and clinical verification of anti-TB drugs.<\/jats:p>","DOI":"10.1093\/bib\/bbae696","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T01:30:08Z","timestamp":1735608608000},"source":"Crossref","is-referenced-by-count":32,"title":["Machine learning\u2013enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9834-9774","authenticated-orcid":false,"given":"Si","family":"Zheng","sequence":"first","affiliation":[{"name":"Institute for Artificial Intelligence & Department of Computer Science and Technology, Tsinghua University , Haidian District, Beijing 100084 ,","place":["China"]},{"name":"Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College , Chaoyang District, Beijing 100020 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaowen","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Chemistry, New York University , New York, NY 10027 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhen","family":"Gu","sequence":"additional","affiliation":[{"name":"National Clinical Laboratory on Tuberculosis, Beijing Key Laboratory on Drug-Resistant Tuberculosis, Beijing Chest Hospital, Capital Medical University , Tongzhou District, Beijing 101149 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yelin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College , Xicheng District, Beijing 100050 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