ML ReBuild: Transfer Learn ML Regression Model using Fingerprints for Small Molecules

This Floe does transfer learning on pre-built ML(machine learning) Neural Network Regression models on properties of small molecules.

The input models are re-trained on 2D fingerprints which will be generated in the Floe itself. Every molecule in the input dataset needs to have a property column to train on (will be ignored otherwise).

It builds machine learning models for all possible combinations of cheminformatics (fingerprint) 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.

By default, the front layers are withheld from training and only the later layers are re-trained on the new data. Change “Freeze Layer” parameter to decide how many top layers to remain constant during training.

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 1,000 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 50,000 molecules. It is recommended to increase the Memory and Disk Space requirements of the cubes to run on larger datasets.

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
Input tensorflow Model	Machine Learning model to predict property	Machine Learing Tensorflow Model Dataset

Outputs

Name	Description	Type
Models Built	Output of Generated Models	Dataset
Failure Output	Output of Failure	Dataset