High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents

Pasquale Linciano, Antonio Quotadamo, Rosaria Luciani, Matteo Santucci, Kimberley M. Zorn, Daniel H. Foil, Thomas R. Lane, Anabela Cordeiro da Silva, Nuno Santarem, Carolina B Moraes, Lucio Freitas-Junior, Ulrike Wittig, Wolfgang Mueller, Michele Tonelli, Stefania Ferrari, Alberto Venturelli, Sheraz Gul, Maria Kuzikov, Bernhard Ellinger, Jeanette Reinshagen, Sean Ekins, Maria Paola Costi

Open source

DOI: 10.1021/acs.jmedchem.3c01322
Published: 2023-11-03
Container: Journal of Medicinal Chemistry
Publisher: American Chemical Society (ACS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1021/acs.jmedchem.3c01322,
  title = {High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents},
  author = {Pasquale Linciano and Antonio Quotadamo and Rosaria Luciani and Matteo Santucci and Kimberley M. Zorn and Daniel H. Foil and Thomas R. Lane and Anabela Cordeiro da Silva and Nuno Santarem and Carolina B Moraes and Lucio Freitas-Junior and Ulrike Wittig and Wolfgang Mueller and Michele Tonelli and Stefania Ferrari and Alberto Venturelli and Sheraz Gul and Maria Kuzikov and Bernhard Ellinger and Jeanette Reinshagen and Sean Ekins and Maria Paola Costi},
  year = {2023},
  journal = {Journal of Medicinal Chemistry},
  doi = {10.1021/acs.jmedchem.3c01322},
  url = {https://doi.org/10.1021/acs.jmedchem.3c01322}
}

RIS

TY  - JOUR
TI  - High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents
AU  - Pasquale Linciano
AU  - Antonio Quotadamo
AU  - Rosaria Luciani
AU  - Matteo Santucci
AU  - Kimberley M. Zorn
AU  - Daniel H. Foil
AU  - Thomas R. Lane
AU  - Anabela Cordeiro da Silva
AU  - Nuno Santarem
AU  - Carolina B Moraes
AU  - Lucio Freitas-Junior
AU  - Ulrike Wittig
AU  - Wolfgang Mueller
AU  - Michele Tonelli
AU  - Stefania Ferrari
AU  - Alberto Venturelli
AU  - Sheraz Gul
AU  - Maria Kuzikov
AU  - Bernhard Ellinger
AU  - Jeanette Reinshagen
AU  - Sean Ekins
AU  - Maria Paola Costi
PY  - 2023
JO  - Journal of Medicinal Chemistry
DO  - 10.1021/acs.jmedchem.3c01322
UR  - https://doi.org/10.1021/acs.jmedchem.3c01322
ER  -

APA

Linciano, P., Quotadamo, A., Luciani, R., Santucci, M., Zorn, K. M., Foil, D. H., Lane, T. R., Silva, A. C. D., Santarem, N., Moraes, C. B., Freitas-Junior, L., Wittig, U., Mueller, W., Tonelli, M., Ferrari, S., Venturelli, A., Gul, S., Kuzikov, M., Ellinger, B., Reinshagen, J., Ekins, S., & Costi, M. P. (2023). High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents. Journal of Medicinal Chemistry. https://doi.org/10.1021/acs.jmedchem.3c01322

Source records

crossref · retrieved 2026-09-27T06:00:50.015Z