Recently, Transformer model, which has achieved great success in many artificial
intelligence fields, has demonstrated its great potential in modeling graph-structured data …

Modern day drug discovery is extremely expensive and time consuming. Although
computational approaches help accelerate and decrease the cost of drug discovery, existing …

The Transformer architecture has gained growing attention in graph representation learning
recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by …

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of
Structure Prediction (CASP14) conference. We explored network architectures that …

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 Attention Networks (GATs) are one of the most popular GNN architectures and are
considered as the state-of-the-art architecture for representation learning with graphs. In …

Despite their widespread success in various domains, Transformer networks have yet to
perform well across datasets in the domain of 3D atomistic graphs such as molecules even …

Enabling effective and efficient machine learning (ML) over large-scale graph data (eg,
graphs with billions of edges) can have a great impact on both industrial and scientific …

Deep graph neural networks (GNNs) have achieved excellent results on various tasks on
increasingly large graph datasets with millions of nodes and edges. However, memory …

The emergence of large-scale pre-trained language models, such as ChatGPT, has
revolutionized various research fields in artificial intelligence. Transformersbased large …