ML Build: Classification Model with Tuner using Fingerprints for Small Molecules
This floe trains multiple ML neural network classification models on the 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 string type property column to train on (such as High or Low). Inputs 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.
The floe generates a Floe Report containing details of the best models built. The user can pick any model built using this floe and use it to predict properties of other molecules in the ML Predict: Classification using Fingerprints for Small Molecules Floe. The Floe Report presents detailed statistics on the hyperparameters, adjusts them, and reruns the floe to build better models (See documentation).
Furthermore, it picks the best models and fine-tunes them using Keras Tuner to optimize those models.
In addition to prediction, the built models provide an explanation of predictions, a confidence interval, and the domain of application.
Warning: By default, this floe builds about 2,500 machine learning models. On a large dataset, this may be expensive, costing greater than $100. 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 should be more than ~200 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.
Name |
Description |
Type |
|---|---|---|
Input Small Molecules to Train
Machine Learning Models on
|
Input dataset file with each record containing a
molecule and response value (String) 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 between Hyperband, RandomSearch, and Bayesian Optimization.
|
String |
Number of Models to Show in Floe Report |
How many best models to provide in the Floe Report. By default, keeps best
20 models (based on Acc) such that it meets memory requirements.
|
Int |
Preprocess Molecule |
For every molecule, stores only largest component, adjusts ionization to neutral pH. |
Bool |
Apply Blockbuster Filter |
Apply Blockbuster filter. |
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 fingerprints. |
IntVec |
Max Radius |
Maximum radius for cheminfo fingerprints. |
IntVec |
Bit Length of Fingerprints (FP) |
Bit length of cheminfo fingerprints. |
IntVec |
Type of Fingerprints (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 layers will be determined by data.
Example: 150,100,50 will create NN with 3 hidden layers of size 150, 100, 50.
|
IntVec |
Sets of Regularization Layers |
List(s) of regularization layers separated by -1.
No regularization on input and output layers.
|
FloatVec |
Learning Rates |
List of all the learning rate hyperparameters to train models. |
FloatVec |
Max Epochs |
Maximum number of epochs to train models. |
Int |
Activation |
Activation Functions: ReLU, LeakyReLU, PReLU, tanh, SELU, ELU. |
List |
Batch Size |
Batch size for training regressor. |
Int |
Adjust Batch Size |
Adjust batch size automatically based on size of training data. |
Name |
Description |
Type |
|---|---|---|
Models Built |
Output of generated models. |
Dataset |
Failure Output |
Output of failure. |
Dataset |