Generates a Flare™ kNN regression or classification model for activity from a set of aligned molecules.
The molecules must be pre-aligned except when using kNN with 2D descriptors (see below): the ‘pyflare’ executable with the ‘align.py’ python script or the 'Flare Align' node can be used to perform the alignment.
The following types of models can be generated.
k Nearest Neighbor (kNN) regression or classification
The kNN methodology is a well-known and robust machine learning approach where the activity for each compound is predicted as the weighted average activity of its k nearest neighbors (most similar compounds) in the training set.
The similarity between the molecules is calculated using either Cresset's 3D field/shape similarity or by using 2D circular fingerprints (ECFP4, ECFP6, FCFP4, or FCFP6).
These kNN models can be used within the 'Flare Score kNN' node (wrapping the 'pyflare' executable) to predict an activity value for newly designed molecules.
Please refer to the Flare manual for a detailed description of the science behind each of these model types in Flare and the corresponding model building options.
This node wraps the 'pyflare' executable, 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 ‘pyflare Path' preference or the CRESSET_PYFLARE_EXE environment variable to point directly at the executable itself.
The Flare Build 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 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.
Options
Basic
Model Settings
Input Ports
- The molecules in the training set which will be used to build the model. All molecules must have activity data and must be pre-aligned unless you are generating a 2D kNN model.
Output Ports
- The input molecules, with tags added for predicted activity. For kNN models the 'distance to model' and 'activity error' information is also added to the molecules.
- The Flare project containing the generated model. The type of Flare project depends on the ‘Flare project format’ option. The 'Flare Project Viewer' node may be used to view the model. The 'Flare Model Info' node may be used to extract data from the model.
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