Machine learning-driven bioavailability prediction in early-stage drug development: a KNIME-based computational workflow for digital health applications.
- DOI
- 10.1080/00498254.2025.2508804
- Published
- 2025 May
- Container
- Xenobiotica; the fate of foreign compounds in biological systems
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1080/00498254.2025.2508804,
title = {Machine learning-driven bioavailability prediction in early-stage drug development: a KNIME-based computational workflow for digital health applications.},
author = {Hammami M and Yeddes W and Gadhoumi H and Yazidi R and Saidani Tounsi M and Msaada K},
year = {2025},
journal = {Xenobiotica; the fate of foreign compounds in biological systems},
doi = {10.1080/00498254.2025.2508804},
url = {https://doi.org/10.1080/00498254.2025.2508804}
}RIS
TY - JOUR TI - Machine learning-driven bioavailability prediction in early-stage drug development: a KNIME-based computational workflow for digital health applications. AU - Hammami M AU - Yeddes W AU - Gadhoumi H AU - Yazidi R AU - Saidani Tounsi M AU - Msaada K PY - 2025 JO - Xenobiotica; the fate of foreign compounds in biological systems DO - 10.1080/00498254.2025.2508804 UR - https://doi.org/10.1080/00498254.2025.2508804 ER -
APA
M, H., W, Y., H, G., R, Y., M, S. T., & K, M. (2025). Machine learning-driven bioavailability prediction in early-stage drug development: a KNIME-based computational workflow for digital health applications.. Xenobiotica; the fate of foreign compounds in biological systems. https://doi.org/10.1080/00498254.2025.2508804
Source records
- pubmed · retrieved 2026-09-26T13:16:06.693Z