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http://hdl.handle.net/2445/152685| Title: | Machine-Learning QSAR Model for Predicting Activity against Malaria Parasite's Ion Pump PfATP4 and In Silico Binding Assay Validation |
| Author: | Lopez Del Rio, Angela Llorach Parés, Laura Perera Lluna, Alexandre Avila, Conxita Nonell Canals, Alfons Sánchez Martínez, Melchor |
| Keywords: | Malalties infeccioses Malària Resistència als medicaments Insectes paràsits Anopheles Communicable diseases Malaria Drug resistance Parasitic insects Anopheles |
| Issue Date: | 8-Sep-2017 |
| Publisher: | MDPI |
| 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. |
| Note: | Reproducció del document publicat a: https://doi.org/10.3390/proceedings1060652 |
| It is part of: | MDPI Proceedings, 2017, vol. 1, num. 16, p. 652 |
| URI: | http://hdl.handle.net/2445/152685 |
| Related resource: | https://doi.org/10.3390/proceedings1060652 |
| ISSN: | 2504-3900 |
| Appears in Collections: | Dades (Institut de Recerca de la Biodiversitat (IRBio)) Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals) |
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