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2017-09-08

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cc-by (c) Lopez Del Rio, Angela et al., 2017
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/152685

Machine-Learning QSAR Model for Predicting Activity against Malaria Parasite's Ion Pump PfATP4 and In Silico Binding Assay Validation

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https://doi.org/10.3390/proceedings1060652

Abstract

Malaria is a mosquito-borne infectious disease caused by parasitic protozoans of the genus Plasmodium. Although different effective antimalarial medicines have been developed, there is serious concern that parasites are developing widespread resistance to these drugs. To avoid this, now the efforts are concentrated on treating the malaria inside the Anopheles mosquito.

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Malalties infeccioses, Malària, Resistència als medicaments, Insectes paràsits, Anopheles

Subject (English)

Communicable diseases, Malaria, Drug resistance, Parasitic insects, Anopheles

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Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)
Dades (Institut de Recerca de la Biodiversitat (IRBio))

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LOPEZ DEL RIO, Angela, et al. Machine-Learning QSAR Model for Predicting Activity against Malaria Parasite's Ion Pump PfATP4 and In Silico Binding Assay Validation. MDPI Proceedings. 2017. Vol. 1, num. 16, pags. 652. ISSN 2504-3900. [consulted: 9 of June of 2026]. Available at: https://hdl.handle.net/2445/152685

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