How to obtain informative representations of molecules is a crucial prerequisite in AI-driven
drug design and discovery. Recent researches abstract molecules as graphs and employ …

How to produce expressive molecular representations is a fundamental challenge in
artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a …

Constructing appropriate representations of molecules lies at the core of numerous tasks
such as material science, chemistry and drug designs. Recent researches abstract …

Molecular graph representation learning has shown considerable strength in molecular
analysis and drug discovery. Due to the difficulty of obtaining molecular property labels, pre …

Molecular machine learning bears promise for efficient molecular property prediction and
drug discovery. However, labelled molecule data can be expensive and time consuming to …

Much of the recent work on learning molecular representations has been based on Graph
Convolution Networks (GCN). These models rely on local aggregation operations and can …

Designing accurate deep learning models for molecular property prediction plays an
increasingly essential role in drug and material discovery. Recently, due to the scarcity of …

Recently, transformer architectures for graphs emerged as an alternative to established
techniques for machine learning with graphs, such as (message-passing) graph neural …

Molecular representation is a critical part of various prediction tasks for physicochemical
properties of molecules and drug design. As graph notations are common in expressing the …

Many applications of machine learning require a model to make accurate pre-dictions on
test examples that are distributionally different from training ones, while task-specific labels …