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- W4313890068 abstract "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–permeability 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." @default.
- W4313890068 created "2023-01-10" @default.
- W4313890068 creator A5023927261 @default.
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- W4313890068 creator A5050532377 @default.
- W4313890068 creator A5060744517 @default.
- W4313890068 date "2023-01-07" @default.
- W4313890068 modified "2023-10-18" @default.
- W4313890068 title "Machine Learning Prediction of Mycobacterial Cell Wall Permeability of Drugs and Drug-like Compounds" @default.
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- W4313890068 doi "https://doi.org/10.3390/molecules28020633" @default.
- W4313890068 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36677691" @default.
- W4313890068 hasPublicationYear "2023" @default.
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