On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction

In order to advance large-scale graph machine learning, the Open Graph Benchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021. The PCQM4M-LSC dataset defines a molecular HOMO-LUMO property prediction task on about 3.8M graphs. In this short paper, we show our current work-in-prog...

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Bibliographic Details
Published in:arXiv.org 2021-06
Main Authors: Edward Elson Kosasih, Cabezas, Joaquin, Sumba, Xavier, Bielak, Piotr, Tagowski, Kamil, Idanwekhai, Kelvin, Tjandra, Benedict Aaron, Jamasb, Arian Rokkum
Format: Article
Language:English
Subjects:
Graph neural networks
Machine learning
Molecular orbitals
Molecular properties
Neural networks
Workflow
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Summary:In order to advance large-scale graph machine learning, the Open Graph Benchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021. The PCQM4M-LSC dataset defines a molecular HOMO-LUMO property prediction task on about 3.8M graphs. In this short paper, we show our current work-in-progress solution which builds an ensemble of three graph neural networks models based on GIN, Bayesian Neural Networks and DiffPool. Our approach outperforms the provided baseline by 7.6%. Moreover, using uncertainty in our ensemble's prediction, we can identify molecules whose HOMO-LUMO gaps are harder to predict (with Pearson's correlation of 0.5181). We anticipate that this will facilitate active learning.
ISSN:2331-8422