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

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


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.

[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center