Forge Build k Nearest Neighbor (kNN)

Generates a k Nearest Neighbor (kNN) regression model using similarity and activity data of aligned molecules.
This node has been deprecated and replaced by the Forge Build Machine Learning node.

The kNN methodology is a well-known and robust distance learning approach where the activity for each new compound is predicted as the weighted average activity of its k nearest neighbors in the training set.

The similarity between the molecules is calculated using Cresset's field/shape similarity method or by using the 2D circular fingerprint methods ECFP4, ECFP6, FCFP4, or FCFP6. This model can be used within the "Forge Score k Nearest Neighbor (kNN)" node with fscore to predict an activity value for newly designed molecules.

Please refer to the Forge manual for a detailed description of the science behind kNN models in Forge and the corresponding model building options.

The molecules must be pre-aligned if Cresset's field/shape similarity method is being used - the falign program or Forge Align node are ideal for this.

This node wraps the Forge Build executable 'fbuild', which must be installed with a valid license for this node to work. If this is installed in the default location on Windows, then it should be found automatically. Otherwise, you must either set the 'Cresset Home' preference or the CRESSET_HOME environment variable to the base Cresset software install directory. You may also set the 'fbuild Path' preference or the CRESSET_FORGEBUILD_EXE environment variable to point directly at the executable itself.

The Forge Build k Nearest Neighbor (kNN) node can be configured to use additional resources to perform calculations. The time taken for the node to run will be drastically reduced using the Cresset's Engine Broker. To use this facility either set the 'Cresset Engine Broker' preference or the CRESSET_BROKER environment variable to point to the location of your local Engine Broker. If you do not currently have the Cresset Engine Broker then contact Cresset (enquiries@cresset-group.com) for pricing on local and cloud based brokers.

For more information visit www.cresset-group.com or contact us at support@cresset-group.com.