Home » MOL2NET-03 » Section 01: Chemistry (All Areas), Soft Matter Physics, Pharmaceutical Sciences, Polymer, and Nanosciences » Paper

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

Biomedical Sciences Department, Health Sciences Division, University of Quintana Roo, UQROO, 77039, Mexico.
3 October 2017
118 views
0/5 rated ( 0 ratings )

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

Cite this article as

Prado-Prado, F. Leave-Species-Out Procedure in Multi-target QSAR models. In Proceedings of the MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition, 15 February–20 December 2017; Sciforum Electronic Conference Series, Vol. 3, 2017 ; doi:10.3390/mol2net-03-04616

Presentation

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