This workflow demonstrates model building for a bioactivity data set with several machine learning methods and binary fingerprints of molecules. The input data needs molecules in an SD file and a column containing the activity (the outcome that should be predicted). A fingerprint type can be chosen and set in the configuration. A report summarizing the input data and the modeling results can be downloaded after the workflow completion.
Used extensions & nodes
Created with KNIME Analytics Platform version 4.5.2 Note: Not all extensions may be displayed.
By using or downloading the workflow, you agree to our terms and conditions.
Discussions are currently not available, please try again later.