Protein–RNA interactions are of vital importance to a variety of cellular activities. Both
experimental and computational techniques have been developed to study the interactions …

The Transformer architecture has become a dominant choice in many domains, such as
natural language processing and computer vision. Yet, it has not achieved competitive …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

We propose a new perspective on designing powerful Graph Neural Networks (GNNs). In a
nutshell, this enables a general solution to inject structural properties of graphs into a …

The identification of compound-protein relations (CPRs), which includes compound-protein
interactions (CPIs) and compound-protein affinities (CPAs), is critical to drug development. A …

The ability of Graph Neural Networks (GNNs) to capture long-range and global topology
information is limited by the scope of conventional graph Laplacian, leading to unsatisfactory …

Being able to identify regions within or around proteins, to which ligands can potentially
bind, is an essential step to develop new drugs. Binding site identification methods can now …

Abstract Background Drug-Drug interactions (DDIs) are a challenging problem in drug
research. Drug combination therapy is an effective solution to treat diseases, but it can also …

Accurately predicting drug-target binding affinity plays a vital role in accelerating drug
discovery. Many computational approaches have been proposed due to costly and time …