ML Regression using Feature Input

ML Regression using Feature Input is a floe that predicts property from a generic pretrained model on custom feature input. It runs a Tensorflow based fully-connected neural network Regression model which needs to be provided by user. Model can be generated using Regression Custom Input builder floe. The molecules predicted need to contain a FloatVec field of same length as the ones used for training. In addition, the floe also uses Convex Box and Monte Carlo Dropout to determine DOA. Finally, it uses Lime, a model agnostic system, to explain the predictions on the molecule. Very cheap and quick. Takes about a cent for property prediction of 20 molecules.

Inputs

Name	Description	Type
Input Small Molecule(s) Dataset to predict property of	The dataset(s) to read records from	Molecule Dataset
Input tensorflow Model	Machine Learning model to predict property	Machine Learing Tensorflow Model Dataset

ML Model Options

Name	Description	Type
Model ID of which Tensorflow model to use to predict	Which model to select. Make sure this matches with input Model ID	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
Number of features to explain	Number of top features to provide LIME explanations for	Int

Explanation and Validation

Name	Description	Type
Property Validation Field	If the dataset has a baseline, the floe reports a comparison between prediction in Floereport	Float
Custom Feature	Field containing feature vector to train model on.	FloatVec

Outputs

Name	Description	Type
Output Property	Output dataset to write to	Dataset
Failure Property	Output dataset to write to	Dataset