Improving Predictions of Molecular Properties with Graph Featurization and Heterogeneous Ensemble Models.

Abstract

We explore a "best-of-both" approach to modeling molecular properties by combining learned molecular descriptors from a graph neural network (GNN) with general-purpose descriptors and a mixed ensemble of machine learning (ML) models. We introduce a MetaModel framework to aggregate predictions from a diverse set of leading ML models. We present a featurization scheme for combining task-specific GNN-derived features with conventional molecular descriptors. We demonstrate that our framework outperforms the cutting-edge ChemProp model on all regression data sets tested and 6 of 9 classification data sets. We further show that including the GNN features derived from ChemProp boosts the ensemble model's performance on several data sets where it otherwise would have underperformed. We conclude that to achieve optimal performance across a wide set of problems, it is vital to combine general-purpose descriptors with task-specific learned features and to use a diverse set of ML models to make the predictions.