Highlights•GNN has attracted wide attention from the field of designing drug molecules.•The
applications of GNN in molecule scoring, molecule generation and optimization, and …

Abstract We present the Open Graph Benchmark (OGB), a diverse set of challenging and
realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine …

Molecule representation learning (MRL) has been extensively studied and current methods
have shown promising power for various tasks, eg, molecular property prediction and target …

Graph neural network (GNN)'s success in graph classification is closely related to the
Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features …

Advancements in neural machinery have led to a wide range of algorithmic solutions for
molecular property prediction. Two classes of models in particular have yielded promising …

Designing a single neural network architecture that performs competitively across a range of
molecule property prediction tasks remains largely an open challenge, and its solution may …

Computational approaches for understanding compound-protein interactions (CPIs) can
greatly facilitate drug development. Recently, a number of deep-learning-based methods …

Deep learning methods for graphs achieve remarkable performance on many node-level
and graph-level prediction tasks. However, despite the proliferation of the methods and their …

This paper presents a new approach for assembling graph neural networks based on
framelet transforms. The latter provides a multi-scale representation for graph-structured …

Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad
applicability to graph-related problems such as quantum chemistry, drug discovery, and high …