ML Predict: Classification using Fingerprints for Small Molecules
Category Paths
Follow one of these paths in the Orion user interface, to find the floe.
Solution-based/Hit to Lead/Properties
Task-based/ADME & Tox Assessment
Solution-based/Hit to Lead/Properties/Model Building
Description
This floe predicts properties of small, drug-like molecules using a pretrained machine learning model. Predictions are based on discreet string classes that the model was trained on.
It runs a TensorFlow-based fully connected neural network regression model for prediction. This user-provided model can be generated using the ML Build: Classification Model with Tuner using Fingerprints for Small Molecules Floe.
The floe uses convex box and Monte Carlo approaches for domain of application predictions. The TensorFlow input dataset also contains a model agnostic system to explain the predictions on the molecule.
It is very cheap and quick, taking only a few cents for the property prediction of 50 molecules.
Outputs:
Failure Data: The molecule (a) is too large or too small or (b) has an unknown atom.
No confidence Data: The molecule’s property falls out of scope of the training set. In this case, the model predicts with no guarantees. The explainer image has a red background.
Success Data: The molecule falls (a) within scope and the explainer has green background or (b) at the edge of scope and the explainer has a yellow background.
Molecules outside the scope of the training set will be sent to the “No Confidence” port, as a prediction cannot be considered reliable. Specifically, the scope is defined as a range in molecular weight, atom count, polar surface area, and calculated logP from the training set molecules. These ranges are given in the Floe Report. If the trained model had the Preprocess Molecule parameter On, it is recommended to set it to On here. More details on how the floe operates can be found in this tutorial and the How-to Guide to Analyze the Machine Learning Predictions .
Promoted Parameters
Title in user interface (promoted name)
Inputs
Input Small Molecule(s) Dataset to predict property of (in): The dataset(s) to read records from
Required
Type: data_source
Input tensorflow Model (tfm): Machine learning model to predict property.
Required
Type: data_source
Outputs
Output Property Prediction for Fingerprint based Classification (out): Output dataset to which to write.
Required
Type: dataset_out
Default: Successful Fingerprint ML Classification Prediction
Failed Property (failed_out): Output dataset to which to write.
Required
Type: dataset_out
Default: Failed Fingerprint ML Classification Prediction
No-confidence Fingerprint ML Classification Prediction (noconf): Output dataset to which to write.
Required
Type: dataset_out
Default: No-confidence Fingerprint ML Classification Prediction
Machine Learning Model Options
Model ID of which Tensorflow model to use to predict. (tfmid): Which model to select. Make sure this matches with input Model ID
Required
Type: integer
Preprocess Molecule (Preprocess Molecule): For every molecule, stores only largest component, adjusts ionization to neutral pH, rejects molecules that fail typecheck
Type: boolean
Default: True
Choices: [True, False]
Apply Blockbuster filter (Blockbuster Filter): Accept or reject molecules based on closeness to Blockbuster molecule properties. For details check toolkit oemolprop.
Type: boolean
Default: False
Choices: [True, False]
Explanation and Validation
Property Validation Field (val): If the input small molecule(s) dataset has a baseline of the physical property, the Floereport provides a comparison between model prediction and baseline. Make sure it is in the exact same unit as the prediction.
Type: field_parameter::string
Molecule Explainer Type (molecule_explainer_type): Select explainer visualisation. Atom: annotate atoms only, Fragment: Annotate Fragments, Combined: Annotate Both
Type: string
Default: Atom
Choices: [‘Combined’, ‘Fragment’, ‘Atom’]