{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T20:26:49Z","timestamp":1792009609263,"version":"4.4.0"},"reference-count":86,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T00:00:00Z","timestamp":1673049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Academic Leadership Program \u201cPriority 2030\u201d of the Lobachevsky State University of Nizhny Novgorod","award":["N-473-99"],"award-info":[{"award-number":["N-473-99"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>The cell wall of Mycobacterium tuberculosis and related organisms has a very complex and unusual organization that makes it much less permeable to nutrients and antibiotics, leading to the low activity of many potential antimycobacterial drugs against whole-cell mycobacteria compared to their isolated molecular biotargets. The ability to predict and optimize the cell wall permeability could greatly enhance the development of novel antitubercular agents. Using an extensive structure\u2013permeability dataset for organic compounds derived from published experimental big data (5371 compounds including 2671 penetrating and 2700 non-penetrating compounds), we have created a predictive classification model based on fragmental descriptors and an artificial neural network of a novel architecture that provides better accuracy (cross-validated balanced accuracy 0.768, sensitivity 0.768, specificity 0.769, area under ROC curve 0.911) and applicability domain compared with the previously published results.<\/jats:p>","DOI":"10.3390\/molecules28020633","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T06:38:27Z","timestamp":1673246307000},"page":"633","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2091-7022","authenticated-orcid":false,"given":"Eugene V.","family":"Radchenko","sequence":"first","affiliation":[{"name":"Department of Chemistry, Lomonosov Moscow State University, 119991 Moscow, Russia"},{"name":"Department of Chemistry, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grigory V.","family":"Antonyan","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Lomonosov Moscow State University, 119991 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6039-3905","authenticated-orcid":false,"given":"Stanislav K.","family":"Ignatov","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8792-8386","authenticated-orcid":false,"given":"Vladimir A.","family":"Palyulin","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Lomonosov Moscow State University, 119991 Moscow, Russia"},{"name":"Department of Chemistry, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Friedman, L.N., Dedicoat, M., and Davies, P.D.O. 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