Multitask Learning On Graph Neural Networks Applied To Molecular Property Predictions

2019/10/29 by Fábio Capela, Capela, Fabio, Vincent Nouchi +7 · 2 citations
Computer Science · Materials Science · #Advanced Graph Neural Networks #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science

paper · pdf · doi:10.48550/arxiv.1910.13124

openalex publication_date 2019/10/29 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28

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.

Cited by

• When Does Global Attention Help? A Unified Empirical Study on Atomistic Graph Learning
• Deep Learning Foundation Models from Classical Molecular Descriptors

Related

• Structured Multi-task Learning for Molecular Property Prediction
• Multi-View Graph Neural Networks for Molecular Property Prediction
• Improving Predictions of Molecular Properties with Graph Featurisation and Heterogeneous Ensemble Models
• Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry
• Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning