Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds.

van Heerden A, Turon G, Duran-Frigola M, Pillay N, Birkholtz LM

DOI: 10.1021/acsomega.3c05664
Published: 2023 Nov 21
Container: ACS omega
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1021/acsomega.3c05664,
  title = {Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds.},
  author = {van Heerden A and Turon G and Duran-Frigola M and Pillay N and Birkholtz LM},
  year = {2023},
  journal = {ACS omega},
  doi = {10.1021/acsomega.3c05664},
  url = {https://doi.org/10.1021/acsomega.3c05664}
}

RIS

TY  - JOUR
TI  - Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds.
AU  - van Heerden A
AU  - Turon G
AU  - Duran-Frigola M
AU  - Pillay N
AU  - Birkholtz LM
PY  - 2023
JO  - ACS omega
DO  - 10.1021/acsomega.3c05664
UR  - https://doi.org/10.1021/acsomega.3c05664
ER  -

APA

A, V. H., G, T., M, D., N, P., & LM, B. (2023). Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds.. ACS omega. https://doi.org/10.1021/acsomega.3c05664

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pubmed · retrieved 2026-09-25T20:28:39.430Z