ML Classification using Fingerprints for Small Molecules¶
ML Classification using Fingerprints for Small Molecules is a floe that predicts property from a generic pretrained model. It runs a Tensorflow based fully-connected neural network Classification model which needs to be provided by user. Model can be generated using Classification builder floe. It uses Convex Box and Monte Carlo technique on the fully-connected neural network for domain of application and error bar prediction. Finally, it uses Lime, a model agnostic system, to explain the predictions on the molecule. Very cheap and quick. Takes about a cent for property prediction of 10 molecules.
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 Learing Tensorflow Model Dataset |
Name |
Description |
Type |
---|---|---|
Model ID of which Tensorflow model to use to predict |
Which model to select. Make sure this matches with input Model ID |
Int |
Preprocess Molecule |
Preprocess by Neutral Ph, Largest Mol, Blockbuster Filter |
Bool |
Apply Blockbuster filter |
For every molecule, stores only largest component, adjusts ionization to Neutral Ph |
Bool |
Name |
Description |
Type |
---|---|---|
Property Validation Field |
If the dataset has a baseline, the floe reports
a comparison between prediction in Floereport
|
Float |
Molecule Explainer Type |
Select explainer visualisation.
Atom: annotate atoms only,
Fragment: Annotate Fragments,
Combined: Annotate Both
|
List |
Name |
Description |
Type |
---|---|---|
Output Property |
Output dataset to write to |
Dataset |
Failure Property |
Output dataset to write to |
Dataset |