Graph Neural Networks (GNNs) have received significant attention for demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

Enzymes, with their specific catalyzed reactions, are necessary for all aspects of life,
enabling diverse biological processes and adaptations. Predicting enzyme functions is …

Protein representation learning plays a crucial role in understanding the structure and
function of proteins, which are essential biomolecules involved in various biological …

Recent years have witnessed great success in handling graph-related tasks with Graph
Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons …

Protein-protein bindings play a key role in a variety of fundamental biological processes,
and thus predicting the effects of amino acid mutations on protein-protein binding is crucial …

Similar to natural language models, pre-trained genome language models are proposed to
capture the underlying intricacies within genomes with unsupervised sequence modeling …

Therapeutic peptides have proven to have great pharmaceutical value and potential in
recent decades. However, methods of AI-assisted peptide drug discovery are not fully …

Recent years have witnessed great success in handling graph-related tasks with graph
neural networks (GNNs). However, most existing GNNs are based on message passing to …

Augmentation is an effective alternative to utilize the small amount of labeled protein data.
However, most of the existing work focuses on design-ing new architectures or pre-training …

Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions,
including signal transduction, transportation, and immune defense. As the accuracy of multi …