Format

Send to

Choose Destination
Bioorg Med Chem Lett. 2015 Jan 1;25(1):100-5. Epub 2014 Nov 7.

Exploiting uncertainty measures in compounds activity prediction using support vector machines.

Author information

1
Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, Kraków 31-343, Poland.

Abstract

The great majority of molecular modeling tasks require the construction of a model that is then used to evaluate new compounds. Although various types of these models exist, at some stage, they all use knowledge about the activity of a given group of compounds, and the performance of the models is dependent on the quality of these data. Biological experiments verifying the activity of chemical compounds are often not reproducible; hence, databases containing these results often possess various activity records for a given molecule. In this study, we developed a method that incorporates the uncertainty of biological tests in machine-learning-based experiments using the Support Vector Machine as a classification model. We show that the developed methodology improves the classification effectiveness in the tested conditions.

PMID:
25466199
DOI:
10.1016/j.bmcl.2014.11.005
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center