Major depressive disorder (MDD) is characterized by persistent depressed mood, loss of
interest or pleasure in previously enjoyable activities, recurrent thoughts of death, and …

Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Develo** personalized diagnostic strategies and targeted treatments requires a deep
understanding of disease biology and the ability to dissect the relationship between …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Effective molecular representation learning is of great importance to facilitate molecular
property prediction. Recent advances for molecular representation learning have shown …

Molecular machine learning bears promise for efficient molecular property prediction and
drug discovery. However, labelled molecule data can be expensive and time consuming to …

Antibiotics are used to fight pathogens but also target commensal bacteria, disturbing the
composition of gut microbiota and causing dysbiosis and disease. Despite this well-known …

Molecular graph representation learning is a fundamental problem in modern drug and
material discovery. Molecular graphs are typically modeled by their 2D topological …

Deep learning models can accurately predict molecular properties and help making the
search for potential drug candidates faster and more efficient. Many existing methods are …