ML Predict: XGBoost for Small Molecules
Category Paths
Follow one of these paths in the Orion user interface, to find the floe.
Solution-based/Hit to Lead/Properties
Task-based/ADME & Tox Assessment
Solution-based/Hit to Lead/Properties/Model Building
Description
This floe predicts a property of small, drug-like molecules using a pretrained machine learning model.
It runs a Scikit-learn based XGBoost model for prediction. This user-provided model can be generated using the floes with preface ML Build. All floes with Model ID 0 is XGBoost so make sure you select those.
All models run on either 2D Fingerprints or Custom User Fingerprints based on what was provided during training. If Custom User Fingerprints were used, make sure to input them in the custom_feature parameter.
The floe is very cheap and quick, taking a few cents for the property prediction of 10 molecules.
Outputs: * Success Data: The prediction using XGBoost.
Promoted Parameters
Title in user interface (promoted name)
Inputs
Input Small Molecule(s) Dataset to predict property of (in): The dataset(s) to read records from
Required
Type: data_source
Input XGBoost Model (xgm): XGBoost Machine learning model to predict property.
Required
Type: data_source
Outputs
Output Property for XGBoost Regression (out): Output dataset to which to write.
Required
Type: dataset_out
Default: Successful XGBoost ML Regression Prediction
Failed Property for XGBoost based Regression (failed_out): Output dataset to which to write.
Required
Type: dataset_out
Default: Failed XGBoost ML Regression Prediction
Machine Learning Model Options
Custom Feature (cf): If model generated using floe ML Build: Regression Model with Tuner using Feature Input, use the same custom feature vector here for prediction.
Type: field_parameter