As powerful tools for representation learning on graphs, graph neural networks (GNNs) have
facilitated various applications from drug discovery to recommender systems. Nevertheless …

We show that viewing graphs as sets of node features and incorporating structural and
positional information into a transformer architecture is able to outperform representations …

This paper studies learning the representations of whole graphs in both unsupervised and
semi-supervised scenarios. Graph-level representations are critical in a variety of real-world …

Molecular representation learning contributes to multiple downstream tasks such as
molecular property prediction and drug design. To properly represent molecules, graph …

We propose a MOTIF-driven contrastive framework to pretrain a graph neural network in a
self-supervised manner so that it can automatically mine motifs from large graph datasets …

Generating molecular graphs with desired chemical properties driven by deep graph
generative models provides a very promising way to accelerate drug discovery process …

Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic
properties remains a formidable challenge for drug discovery. Deep learning provides us …

Graph neural networks (GNNs) have become the standard learning architectures for graphs.
GNNs have been applied to numerous domains ranging from quantum chemistry …

Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a
central problem of small-molecule based drug discovery. Molecules can be represented as …

Abstract Models based on machine learning can enable accurate and fast molecular
property predictions, which is of interest in drug discovery and material design. Various …