ML Build: Regression Model with Tuner using Fingerprints for Small Molecules¶
This Floe trains multiple ML(machine learning) Neural Network Regression models on physical properties of small molecules.
The models train on 2D fingerprints which will be generated in the Floe itself. Every molecule in the input dataset needs to have a physical property column to train on (will be ignored otherwise).
It builds machine learning models for all possible combinations of cheminformatics (fingerprint) and neural network hyperparameters provided in the Advanced sections. Read the documentation to learn more about these parameters and how they should be set for a given training data set.
Further, it picks the best models and fine tunes them using keras tuner to provide best models.
Generates a Floe report containing details of the best models built. User can pick any model and use it to predict properties of other molecules in the Floe ML Regression using Fingerprints for Small Molecules. The Floe report presents detailed statistics on the hyperparameters, adjust them and rerun the Floe to build better models (See documentation).
In addition to prediction, the built models provide an explanation of predictions, a confidence interval, and the domain of application.
Warning: This Floe by default, builds about 2,000 machine learning models. On a large dataset, this may be expensive. Since multiple parameters lead to this cost, refer to documentation on how to build a cheaper version for practice. The dataset size to build decent models needs to be at least a 100 molecules (barring exceptions). We have stress tested up to 30,000 molecules. It is recommended to increase the Memory and Disk Space requirements of the cubes to run on larger datasets. Please refer to docs on how to build models on a larger dataset.
Name |
Description |
Type |
|---|---|---|
Input Small Molecules to train
machine learning models on.
|
Input dataset file with each record containing
molecule and response value(float) to train on
|
Molecule Dataset |
Name |
Description |
Type |
|---|---|---|
Response Value Field |
Name of the field containing the primary data being trained on and predicted. |
Float |
Are we using Keras tuner |
If this is on, we fine tune our algorithm using the keras tuner
|
Bool |
What kind of keras tuner to use |
Choose betweem Hyperband, RandomSearch, Bayesian Optimization
|
String |
Number of Models to show in Floe report |
How many best models to provide in FloeReport. By default, keeps best
20 models (based on r2 score) such that it meet memory requirement
|
Int |
Are we training tensorflow probability |
True: Builds Tensorflow Probability based Neural Network Model for
finding the Domain of Application/ Error Bar,
False: Build Tensorflow Neural Network Model for prediction and explanation
|
Bool |
Preprocess Molecule |
For every molecule, stores only largest component, adjusts ionization to Neutral Ph |
Bool |
Apply Blockbuster filter |
Apply blockbuster filter |
Bool |
Negative Log |
Transform Learning Value to Negative Log.
Only for regression. False: Build Tensorflow Neural Network Model
for prediction and explanation (Deterministic Model)
|
Bool |
Molecule Explainer Type |
Select explainer visualization. Atom: annotate atoms only,
Fragment: Annotate Fragments, Combined: Annotate Both
|
List |
Name |
Description |
Type |
|---|---|---|
Min Radius |
Minimum radius for cheminfo fingerprint. |
IntVec |
Max Radius |
Maximum radius for cheminfo fingerprint. |
IntVec |
Bit Length of FP |
Bit Length of cheminfo fingerprint |
IntVec |
Type of FP |
Type of cheminfo fingerprints |
IntVec |
Name |
Description |
Type |
|---|---|---|
Dropouts |
List of dropout hyperparameters. |
FloatVec |
Sets of Hidden Layers |
list(s) of hidden layers separated by -1. Input and output layer will be determined by data.
Eg: 150,100,50 will create NN with 3 hidden layers of size 150, 100, 50.
|
IntVec |
Sets of Regularisation Layers |
list(s) of regularisation layers separated by -1.
No regularisation on Input and output layer.
|
FloatVec |
Learning Rates |
List of all the learning rate hyperparameters to train model. |
FloatVec |
Max Epochs |
Maximum number of epochs to train model. |
Int |
Activation |
Activation Functions: ReLU, LeakyReLU, PReLU, tanh, SELU, ELU |
List |
Batch Size |
Batch size for training regressor |
Int |
Name |
Description |
Type |
|---|---|---|
Models Built |
Output of Generated Models |
Dataset |
Failure Output |
Output of Failure |
Dataset |