Release Notes¶

v0.10.5 April 2023¶

This is the first minor release of Model Building package with package version 0.10.5.
This package is built using OpenEye-orionplatform==4.4.0, OpenEye-toolkits==2022.2.1, and OpenEye-Snowball==0.24.0.
Added Overfit correlation and coefficients [refer to Optimization Strategy V] to train models without overfit.
Added Precision, Recall, Specificity, and Sensitivity parameters in floe report for classification model building Floes. These fields are also available in record output.

This is the second major release of Model Building package with package version 0.10.0.
This package is built using OpenEye-orionplatform==4.5.4, OpenEye-toolkits==2022.2.1, and OpenEye-Snowball==0.24.0..
All floes have a new brief parameter and floe categories.

ML Classifier Model Building using Fingerprints for Small Molecules trains multiple Neural Network Classification models on physical properties of small molecules. Uses cheminformatics and machine learning to train said models.
ML Classification using Fingerprints for Small Molecules predicts discreet classes from a pretrained neural network model. Needs molecule database and neural network model dataset as input (trained using the previous floe).
ML Regression Model Building using Feature Input trains multiple Neural Network Regression models on physical properties of small molecules. Uses user provided custom feature vector and machine learning to train said models.
ML Regression using Feature Input predicts property from a pretrained neural network model. Needs molecule database, feature vector(same length as training data), and neural network model dataset as input (trained using the previous floe).
Small Molecule Data Processing for ML Model Building analyzes and preprocesses data for training ML(machine learning) models. Needs molecule database and response value (either for classification or regression).

ML Regression Model Building using Fingerprints for Small Molecules (previously Neural Network Based Regression Model Building) trains multiple Neural Network (Full or Probabilistic) Regression models on physical properties of small molecules. Uses cheminformatics and machine learning to train said models.
ML Regression using Fingerprints for Small Molecules (previously Physical Property Prediction for Small Molecules using Machine Learning) predicts property from a pretrained neural network model. Needs molecule database and neural network model dataset as input (trained using the previous floe).
Solubility Prediction for Small Molecules using Machine Learning and Cheminfo Fingerprints predicts solubility in log uM using neural network based machine learning. Needs molecule database as input.

This package is built using OpenEye-orionplatform==4.4.0, OpenEye-toolkits==2022.1.1, and OpenEye-Snowball==0.24.0..
Bug fixed on Physical Property Prediction for Small Molecules using Machine Learning
Model and Record ID in Neural Network Based Regression Model Building renamed for user convenience.
Better use case handling for wrong inputs.
Frequently Asked Questions section added

This is the first release of Model Building package with package version 0.9.0.
This package is built using OpenEye-orionplatform==4.4.0, OpenEye-toolkits==2022.1.1, and OpenEye-Snowball==0.24.0..
All floes have a new brief parameter and floe categories.

Neural Network Based Regression Model Building trains multiple Neural Network (Full or Probabilistic) Regression models on physical properties of small molecules. Uses cheminformatics and machine learning to train said models.
Physical Property Prediction for Small Molecules using Machine Learning predicts property from a pretrained neural network model. Needs molecule database and neural network model dataset as input (trained using the previous floe).
Solubility Prediction for Small Molecules using Machine Learning and Cheminfo Fingerprints predicts solubility in log uM using neural network based machine learning. Needs molecule database as input.