Journal
BIOORGANIC & MEDICINAL CHEMISTRY LETTERS
Volume 25, Issue 1, Pages 100-105Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.bmcl.2014.11.005
Keywords
Uncertainty of in vitro tests; Machine learning; Support Vector Machine; Weighting protocol; ChEMBLdb
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Funding
- National Science Centre, Poland [2013/09/N/ST6/03015]
- EXtension of academia-based PLATFORM to antidepressant hits discovery (PLATFORMEX)
- National Centre for Research and Development within the Polish-Norwegian Research Programme [Pol-Nor/198887/73/2013]
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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. (C) 2014 Published by Elsevier Ltd.
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