Validation of AI-enabled surrogate models in quantitative systems pharmacology: a practical, context-of-use-driven review.

Goryanin I, Goryanin I

DOI: 10.1016/j.drudis.2026.104729
Published: 2026 Jul
Container: Drug discovery today
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/j.drudis.2026.104729,
  title = {Validation of AI-enabled surrogate models in quantitative systems pharmacology: a practical, context-of-use-driven review.},
  author = {Goryanin I and Goryanin I},
  year = {2026},
  journal = {Drug discovery today},
  doi = {10.1016/j.drudis.2026.104729},
  url = {https://doi.org/10.1016/j.drudis.2026.104729}
}

RIS

TY  - JOUR
TI  - Validation of AI-enabled surrogate models in quantitative systems pharmacology: a practical, context-of-use-driven review.
AU  - Goryanin I
AU  - Goryanin I
PY  - 2026
JO  - Drug discovery today
DO  - 10.1016/j.drudis.2026.104729
UR  - https://doi.org/10.1016/j.drudis.2026.104729
ER  -

APA

I, G., & I, G. (2026). Validation of AI-enabled surrogate models in quantitative systems pharmacology: a practical, context-of-use-driven review.. Drug discovery today. https://doi.org/10.1016/j.drudis.2026.104729

Source records

pubmed · retrieved 2026-09-26T00:40:13.258Z