From empirical imprinting to programmable synthetic receptors: a perspective on data science and machine learning for molecularly imprinted polymers.
- DOI
- 10.1039/d6py00493h
- Published
- 2026 Sep 15
- Container
- Polymer chemistry
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1039/d6py00493h,
title = {From empirical imprinting to programmable synthetic receptors: a perspective on data science and machine learning for molecularly imprinted polymers.},
author = {Alsholi A and Tiwari S and Zhang J and Peeters M},
year = {2026},
journal = {Polymer chemistry},
doi = {10.1039/d6py00493h},
url = {https://doi.org/10.1039/d6py00493h}
}RIS
TY - JOUR TI - From empirical imprinting to programmable synthetic receptors: a perspective on data science and machine learning for molecularly imprinted polymers. AU - Alsholi A AU - Tiwari S AU - Zhang J AU - Peeters M PY - 2026 JO - Polymer chemistry DO - 10.1039/d6py00493h UR - https://doi.org/10.1039/d6py00493h ER -
APA
A, A., S, T., J, Z., & M, P. (2026). From empirical imprinting to programmable synthetic receptors: a perspective on data science and machine learning for molecularly imprinted polymers.. Polymer chemistry. https://doi.org/10.1039/d6py00493h
Source records
- pubmed · retrieved 2026-09-25T21:14:54.562Z