title: Exploiting uncertainty measures in compounds activity prediction using support vector machines.
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.
authors.affiliation: Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, Kraków 31-343, Poland.
authors.forename: Sabina
authors.initials: S
authors.lastname: Smusz
authors.forename: Wojciech Marian
authors.initials: WM
authors.lastname: Czarnecki
authors.forename: Dawid
authors.initials: D
authors.lastname: Warszycki
authors.forename: Andrzej J
authors.initials: AJ
authors.lastname: Bojarski
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D005544:Forecasting; D006801:Humans; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D051381:Rats; D060388:Support Vector Machine; D035501:Uncertainty
chemical_list: D004364:Pharmaceutical Preparations