Multi-representation machine learning approaches for screening aurora kinases A & B inhibitors and insights from structural alerts.
- DOI
- 10.1016/j.jmgm.2026.109365
- Published
- 2026 Jul
- Container
- Journal of molecular graphics & modelling
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jmgm.2026.109365,
title = {Multi-representation machine learning approaches for screening aurora kinases A \& B inhibitors and insights from structural alerts.},
author = {Kumar A and Chutia H and Nagamani S},
year = {2026},
journal = {Journal of molecular graphics \& modelling},
doi = {10.1016/j.jmgm.2026.109365},
url = {https://doi.org/10.1016/j.jmgm.2026.109365}
}RIS
TY - JOUR TI - Multi-representation machine learning approaches for screening aurora kinases A & B inhibitors and insights from structural alerts. AU - Kumar A AU - Chutia H AU - Nagamani S PY - 2026 JO - Journal of molecular graphics & modelling DO - 10.1016/j.jmgm.2026.109365 UR - https://doi.org/10.1016/j.jmgm.2026.109365 ER -
APA
A, K., H, C., & S, N. (2026). Multi-representation machine learning approaches for screening aurora kinases A & B inhibitors and insights from structural alerts.. Journal of molecular graphics & modelling. https://doi.org/10.1016/j.jmgm.2026.109365
Source records
- pubmed · retrieved 2026-09-26T15:17:05.489Z