Exploiting uncertainty measures in compounds activity prediction using support vector machines.
Journal:
Bioorganic & medicinal chemistry letters
Published Date:
Nov 7, 2014
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.