ML Predict: XGBoost for Small Molecules

This floe predicts a property of small, drug-like molecules using a pretrained machine learning model. The XGBoost models built by default using ML Build floes can be used as input here. Any of the floes with the preface ML Build will build this model. Use this floe to predict a property of small molecules using XGBoost from the ML Build floes. It will act as a classifier if the base model is a classifier trained on String and will act as a regressor for Float. The floe autoselects the type of prediction based on the provided model. If the model was trained on a custom feature vector, please provide a field in the input molecule dataset containing the feature vector; otherwise, the floe will fail.

It runs a Scikit-learn based XGBoost model for prediction. This user-provided model can be generated using the floes with the preface ML Build. All floes with Model ID 0 have XGBoost, so be sure to select those floes.

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 inexpensive and quick, taking a few cents for the property prediction of 10 molecules.

Outputs:

• Success Data: The prediction using XGBoost.