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Proceedings 2017, 1(6), 652; doi:10.3390/proceedings1060652

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

Mind the Byte S.L., 08028 Barcelona, Spain
Department of ESAII, Center for Biomedical Engineering Research, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain
Department of Evolutionary Biology, Ecology, and Environmental Sciences, Faculty of Biology and Biodiversity Research Institute (IrBIO), Universitat de Barcelona, 08028 Barcelona, Spain
Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), 28029 Madrid, Spain
Presented at the 1st Molecules Medicinal Chemistry Symposium, Barcelona, Spain, 8 September 2017.
Author to whom correspondence should be addressed.
Published: 18 October 2017
(This article belongs to the Proceedings of the 1st Molecules Medicinal Chemistry Symposium)
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Note: In lieu of an abstract, this is an excerpt from the first page.


Malaria is a mosquito-borne infectious disease caused by parasitic protozoans of the genus Plasmodium. [...]
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Rio, A.-D.; Llorach-Parés, L.; Perera-Lluna, A.; Avila, C.; Nonell-Canals, A.; Sanchez-Martinez, M. Machine-Learning QSAR Model for Predicting Activity against Malaria Parasite’s Ion Pump PfATP4 and In Silico Binding Assay Validation. Proceedings 2017, 1, 652.

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