Next Article in Journal
Docking Study on T. cruzi Trypanothione Reductase and Iron-Superoxide Dismutase Isoforms of a Series of Imidazole-Based Derivatives as an Approach towards the Design of New Potential Inhibitors
Previous Article in Journal
Current Pipeline of Antimalarial Therapies
Article Menu
Issue 6 (MMCS 2017) cover image

Article Versions

Export Article

Open AccessAbstract
Proceedings 2017, 1(6), 652; doi:10.3390/proceedings1060652 (registering DOI)

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

1
Mind the Byte S.L., 08028 Barcelona, Spain
2
Department of ESAII, Center for Biomedical Engineering Research, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain
3
Department of Evolutionary Biology, Ecology, and Environmental Sciences, Faculty of Biology and Biodiversity Research Institute (IrBIO), Universitat de Barcelona, 08028 Barcelona, Spain
4
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)
Download PDF [123 KB, uploaded 18 October 2017]
Note: In lieu of an abstract, this is an excerpt from the first page.

Excerpt

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Proceedings EISSN 2504-3900 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top