ML Regression Model Building using Feature Input

‘ML Regression Model Building using Feature Input’ is a floe that train multiple Neural Network Regression models on custom feature input provided by the user. Every input training record needs to contain a feature vector as FloatVec which will be used to train the models. These feature vectors will be normalized before training. It builds Machine Learning models for all possible combination of neural network hyperparameters provided below. Generates floe report containing details of the best models built. User can pick any model and use it to predict properties of other molecules in a separate floe (Predict Physical Properties). The floe report presents detailed statistics on the hyperparameters so as to tweak them and build better models (See documentation). In addition to prediction, the built models provide explanation of predictions and confidence interval. NOTE: This floe by default, builds about 1k machine learning models. Although cheaper than generating fingerprints, on a large dataset, this maybe pricey. Refer to documentations on how to build a cheaper version of the same

Inputs

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

Options

Name

Description

Type

Response Value Field

Name of the field containing the primary data being trained on and predicted.

Float

Custom Feature
Field containing feature vector to train model on. Must be a float vector

FloatVec

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

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

Negative Log

Transform Learning Value to Negative Log.
Only for Regression. False: Build Tensorflow Neural Network Model
for prediction and explanation (Deterministic Model)

Bool

Number of Top features to explain

Number of top features to provide LIME votes results for.
These are the most important features that determine property prediction based on trained ML Model

List

Neural Network Hyperparameter Options: Build models for all possible combination of Hyperparameters

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

Outputs

Name

Description

Type

Models Built

Output of Generated Models

Dataset

Failure Output

Output of Failure

Dataset