ML Predict: Classification using Fingerprints for Small Molecules

This floe predicts properties of small, drug-like molecules using a pretrained ML 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 prediction. The input TensorFlow dataset also contains a model agnostic system to explain the predictions on the molecule.

The floe is very cheap and quick, costing 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 a 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 reported in the Floe Report. If the trained model had the Preprocess Molecule parameter On, it is recommended to do set it On here as well.

Inputs

Name	Description	Type
Input Small Molecule(s) Dataset to Predict Property of	The dataset(s) to read records from.	Molecule Dataset
Input TensorFlow Model	Machine learning model to predict property.	Machine Learning TensorFlow Model Dataset

ML Model Options

Name	Description	Type
Model ID of TensorFlow model to Use to Predict*	Which model to select. Make sure this matches the input model ID.	Int
Preprocess Molecule	Preprocess by neutral pH, largest molecule.	Bool
Apply Blockbuster Filter	For every molecule, stores only largest component, adjusts ionization to neutral pH.	Bool

Explanation and Validation

Name	Description	Type
Property Validation Field	If the dataset has a baseline, the floe reports a comparison between predictions in the Floe Report.	Float
Molecule Explainer Type	Select explainer visualization. Atom: annotate atoms only Fragment: annotate fragments Combined: annotate both	List

Outputs

Name	Description	Type
Output Property	Output dataset to write to.	Dataset
Failure Property	Output dataset to write to.	Dataset