ML Classifier Model Building using Fingerprints for Small Molecules

‘ML Classifier Model Building using Fingerprints for Small Molecules’ is a floe that train multiple Neural Network Classifier models on physical properties of small molecules. It builds Machine Learning models for all possible combination of cheminformatics and 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. 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(String) 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
Number of Models to show in Floe report	How many best models to provide in FloeReport. By default, keeps best 20 models (based on Acc) 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
Molecule Explainer Type	Select explainer visualization. Atom: annotate atoms only, Fragment: Annotate Fragments, Combined: Annotate Both	List

Cheminfo Fingerprint Options: Build models for all possible combination of Fingerprints

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

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