Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions

TL;DR

This paper introduces a multitask learning approach using graph neural networks to improve molecular property prediction, demonstrating enhanced performance and reduced variance, especially on small datasets.

Contribution

It presents a novel multitask learning framework for GNNs that outperforms existing models in molecular property prediction tasks.

Findings

Multitask learning improves GNN performance.

Significant reduction in model variance.

Better results on small datasets without data augmentation.

Abstract

Prediction of molecular properties, including physico-chemical properties, is a challenging task in chemistry. Herein we present a new state-of-the-art multitask prediction method based on existing graph neural network models. We have used different architectures for our models and the results clearly demonstrate that multitask learning can improve model performance. Additionally, a significant reduction of variance in the models has been observed. Most importantly, datasets with a small amount of data points reach better results without the need of augmentation.