Exploiting uncertainty measures in compounds activity prediction using support vector machines

• Published in: Bioorganic & medicinal chemistry letters Vol. 25; no. 1; pp. 100 - 105
• Main Authors: Smusz, Sabina, Czarnecki, Wojciech Marian, Warszycki, Dawid, Bojarski, Andrzej J.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.01.2015
• Subjects: Animals; Artificial Intelligence; ChEMBLdb; Forecasting; Humans; Machine learning; Pharmaceutical Preparations - chemistry; Rats; Support Vector Machine; Uncertainty; Uncertainty of in vitro tests; Weighting protocol; Support Vector Machine; ChEMBLdb; Weighting protocol; Uncertainty of in vitro tests; Machine learning
• ISSN: 0960-894X; 1464-3405
• DOI: 10.1016/j.bmcl.2014.11.005

[Display omitted] 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.