EventsMOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.
Event submissions
Published
with-doi10.3390/mol2net-03-04616
This submission belongs to the session 01. CHEMBIOINFO-03: Chem-Bioinformatics Congress Cambridge, UK-Chapel Hill and Richmond, USA, 2017 of the event MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed.
Published date
03 Oct, 2017
Citation
Francisco J Prado-Prado, Leave-Species-Out Procedure in Multi-target QSAR models, in Proceedings of MOL2NET'17, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 3rd ed., 15 January–15 December 2017, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-03-04616
Share
Email
Facebook
Twitter
LinkedIn

Leave-Species-Out Procedure in Multi-target QSAR models

Francisco J Prado-Prado 1
1. Biomedical Sciences Department, Health Sciences Division, University of Quintana Roo, UQROO, 77039, Mexico.
Abstract

In this paper we generalized QSAR models to predict the biological activity of antifungal drugs against 87 fungi species. The data was processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active. The model correctly classifies 338 out of 368 active compounds (91.85%) and 89 out of 123 non-active compounds (72.36%). Overall training predictability was 86.97% (427 out of 491 compounds). Validation of the model was carried out by means of Leave-Species-Out (LSO) procedure. After elimination step-by-step of all drugs tested against one specific species we record the percentage of good classification of leave-out compounds (LSO-predictability).

Keywords
Medicinal Chemistry
QSAR models
Model validation
Multi-target model
Poster
prado.pdf
prado.pdf
Trends in Medicinal Chemistry
Ethnopharmacology, biological activity and chemical characterization of Mansoa alliacea. A review about a promising plant from Amazonian region