Systematic Artifacts in Support Vector Regression-Based Compound Potency Prediction Revealed by Statistical and Activity Landscape Analysis

Open Access

Peer-reviewed

Research Article

Jenny Balfer,

Affiliation Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113, Bonn, Germany
⨯
Jürgen Bajorath

* E-mail: bajorath@bit.uni-bonn.de

Affiliation Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113, Bonn, Germany
⨯

Reader Comments (0)

SARS
Is the Subject Area "SARS" applicable to this article?

Thanks for your feedback.
Forecasting
Is the Subject Area "Forecasting" applicable to this article?

Thanks for your feedback.
Support vector machines
Is the Subject Area "Support vector machines" applicable to this article?

Thanks for your feedback.
Cliffs
Is the Subject Area "Cliffs" applicable to this article?

Thanks for your feedback.
Serotonin
Is the Subject Area "Serotonin" applicable to this article?

Thanks for your feedback.
Computational chemistry
Is the Subject Area "Computational chemistry" applicable to this article?

Thanks for your feedback.
Kernel functions
Is the Subject Area "Kernel functions" applicable to this article?

Thanks for your feedback.
Statistical data
Is the Subject Area "Statistical data" applicable to this article?

Thanks for your feedback.