Blood–Brain Barrier Permeability Prediction by Integrating Deep Learning Representations with Chemical Compound Knowledge
- DOI
- 10.1021/acsomega.6c01181
- Published
- 2026-10-22
- Container
- ACS Omega
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acsomega.6c01181,
title = {Blood–Brain
Barrier Permeability Prediction
by Integrating Deep Learning Representations with Chemical Compound
Knowledge},
author = {Nadine Grant and Diego Machado Reyes and Zefan Yang and Alec Wang and Leo Q. Wan and Chunyu Wang and Pingkun Yan},
year = {2026},
journal = {ACS Omega},
doi = {10.1021/acsomega.6c01181},
url = {https://doi.org/10.1021/acsomega.6c01181}
}RIS
TY - JOUR TI - Blood–Brain Barrier Permeability Prediction by Integrating Deep Learning Representations with Chemical Compound Knowledge AU - Nadine Grant AU - Diego Machado Reyes AU - Zefan Yang AU - Alec Wang AU - Leo Q. Wan AU - Chunyu Wang AU - Pingkun Yan PY - 2026 JO - ACS Omega DO - 10.1021/acsomega.6c01181 UR - https://doi.org/10.1021/acsomega.6c01181 ER -
APA
Grant, N., Reyes, D. M., Yang, Z., Wang, A., Wan, L. Q., Wang, C., & Yan, P. (2026). Blood–Brain Barrier Permeability Prediction by Integrating Deep Learning Representations with Chemical Compound Knowledge. ACS Omega. https://doi.org/10.1021/acsomega.6c01181
Source records
- crossref · retrieved 2026-11-09T04:09:24.952Z