ML Regression using Fingerprints for Small Molecules¶
ML Regression 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 Regression model which needs to be provided by user. Model can be generated using Regression builder floe. If provided, uses Tensorflow based probabilistic fully-connected neural network for domain of application (DOA) and error bar prediction. In addition, the floe also uses Convex Box and Monte Carlo Dropout to determine DOA. 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 |
Input tensorflow probability Model |
The dataset(s) to read records from |
Machine Learing Tensorflow Probaility 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 |
Model ID of which Tensorflow Probability (TFP) 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 |