Interpretable Drug–Target Prediction via Geometric Alignment: A Linear Alternative to Deep Architectural Complexity

Aashutosh Ghimire, Chris Alvin, Adam Bess, Supratik Mukhopadhyay

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DOI: 10.1002/minf.70060
Published: 2026-10-29
Container: Molecular Informatics
Publisher: Wiley
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1002/minf.70060,
  title = {Interpretable Drug–Target Prediction via Geometric Alignment: A Linear Alternative to Deep Architectural Complexity},
  author = {Aashutosh Ghimire and Chris Alvin and Adam Bess and Supratik Mukhopadhyay},
  year = {2026},
  journal = {Molecular Informatics},
  doi = {10.1002/minf.70060},
  url = {https://doi.org/10.1002/minf.70060}
}

RIS

TY  - JOUR
TI  - Interpretable Drug–Target Prediction via Geometric Alignment: A Linear Alternative to Deep Architectural Complexity
AU  - Aashutosh Ghimire
AU  - Chris Alvin
AU  - Adam Bess
AU  - Supratik Mukhopadhyay
PY  - 2026
JO  - Molecular Informatics
DO  - 10.1002/minf.70060
UR  - https://doi.org/10.1002/minf.70060
ER  -

APA

Ghimire, A., Alvin, C., Bess, A., & Mukhopadhyay, S. (2026). Interpretable Drug–Target Prediction via Geometric Alignment: A Linear Alternative to Deep Architectural Complexity. Molecular Informatics. https://doi.org/10.1002/minf.70060

Source records

crossref · retrieved 2026-11-10T06:37:11.853Z