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

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Citation: J. Med. Chem. 66(22):15230-15255.

Date Published: 3rd Nov 2023

Registered Mode: by DOI

Authors: 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

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Linciano, P., Quotadamo, A., Luciani, R., Santucci, M., Zorn, K. M., Foil, D. H., Lane, T. R., Cordeiro da Silva, A., Santarem, N., B Moraes, C., Freitas-Junior, L., Wittig, U., Mueller, W., Tonelli, M., Ferrari, S., Venturelli, A., Gul, S., Kuzikov, M., Ellinger, B., … Costi, M. P. (2023). High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents. In Journal of Medicinal Chemistry (Vol. 66, Issue 22, pp. 15230–15255). American Chemical Society (ACS). https://doi.org/10.1021/acs.jmedchem.3c01322
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Created: 14th Jul 2026 at 06:38

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