Artificial intelligence-driven cross-endpoint stacked ensemble learning for separate but meta-feature coupled prediction of six human pharmacokinetic endpoints with RDKit molecular representation and applicability domain control on real TDC benchmark data
- DOI
- 10.1007/s00210-026-05785-w
- Published
- 2026-08-07
- Container
- Naunyn-Schmiedeberg's Archives of Pharmacology
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s00210-026-05785-w,
title = {Artificial intelligence-driven cross-endpoint stacked ensemble learning for separate but meta-feature coupled prediction of six human pharmacokinetic endpoints with RDKit molecular representation and applicability domain control on real TDC benchmark data},
author = {Oluwaseun E. Agboola and Samuel S. Agboola},
year = {2026},
journal = {Naunyn-Schmiedeberg's Archives of Pharmacology},
doi = {10.1007/s00210-026-05785-w},
url = {https://doi.org/10.1007/s00210-026-05785-w}
}RIS
TY - JOUR TI - Artificial intelligence-driven cross-endpoint stacked ensemble learning for separate but meta-feature coupled prediction of six human pharmacokinetic endpoints with RDKit molecular representation and applicability domain control on real TDC benchmark data AU - Oluwaseun E. Agboola AU - Samuel S. Agboola PY - 2026 JO - Naunyn-Schmiedeberg's Archives of Pharmacology DO - 10.1007/s00210-026-05785-w UR - https://doi.org/10.1007/s00210-026-05785-w ER -
APA
Agboola, O. E., & Agboola, S. S. (2026). Artificial intelligence-driven cross-endpoint stacked ensemble learning for separate but meta-feature coupled prediction of six human pharmacokinetic endpoints with RDKit molecular representation and applicability domain control on real TDC benchmark data. Naunyn-Schmiedeberg's Archives of Pharmacology. https://doi.org/10.1007/s00210-026-05785-w
Source records
- crossref · retrieved 2026-11-11T03:51:55.803Z