# Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions

arXiv: https://arxiv.org/abs/1910.13124 · HTML: https://mlsanity.com/paper/1910.13124/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Deep Graph Reprogramming](https://mlsanity.com/paper/2304.14593/) (2023)
- [Deep Learning Foundation Models from Classical Molecular Descriptors](https://mlsanity.com/paper/2506.15792/) (2025)
- [On the relationship between disentanglement and multi-task learning](https://mlsanity.com/paper/2110.03498/) (2021)
- [Learning to Group Auxiliary Datasets for Molecule](https://mlsanity.com/paper/2307.04052/) (2023)
- [Graph Neural Networks for Surfactant Multi-Property Prediction](https://mlsanity.com/paper/2401.01874/) (2024)
- [When Does Global Attention Help? A Unified Empirical Study on Atomistic Graph Learning](https://mlsanity.com/paper/2510.05583/) (2025)

## Abstract (arXiv)

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.