EventsMOL2NET'18, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 4th ed.
Event submissions
Published
with-doi10.3390/mol2net-04-06141
This submission belongs to the session 06. CHEMBIOMOL-04: Chem. Biol. & Med. Chem. Workshop, Paraiba, Porto, Rostock, Germany-Galveston, Texas, USA, 2018 of the event MOL2NET'18, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 4th ed.
Published date
17 Jan, 2019
Citation
Naivi Flores Balmaseda, Susana Rojas Socarrás, Juan Alberto Castillo Garit, Machine learning techniques and the identification of new potentially active compounds against <em>Leishmania infantum</em>., in Proceedings of MOL2NET'18, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 4th ed., 15 January 2018–20 January 2019, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-04-06141
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Machine learning techniques and the identification of new potentially active compounds against Leishmania infantum.

Naivi Flores Balmaseda 1
Susana Rojas Socarrás 2
Juan Alberto Castillo Garit 3
1. Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of Chemical-Pharmacy. Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba
2. Department of Pharmacy, Faculty of Chemical-Pharmacy. Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
3. Unit of Computer-Aided Molecular “Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit). Central University of Las Villas Unit of Experimental Toxicology, University of Medical Sciences "Dr. Serafín Ruiz de Zarate Ruiz "Villa, Clara. Cuba
Abstract
Keywords
Leishmaniasis
machine learning techniques
protozoo
WEKA software
Leishmania infantum
amastigote.