Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …

Deep learning models can accurately predict molecular properties and help making the
search for potential drug candidates faster and more efficient. Many existing methods are …

Accurate molecular property prediction, as one of the classical cheminformatics topics, plays
a prominent role in the fields of computer-aided drug design. For instance, property …

Drug discovery often relies on the successful prediction of protein-ligand binding affinity.
Recent advances have shown great promise in applying graph neural networks (GNNs) for …

Unlike vision and language data which usually has a unique format, molecules can naturally
be characterized using different chemical formulations. One can view a molecule as a 2D …

Recently many efforts have been devoted to applying graph neural networks (GNNs) to
molecular property prediction which is a fundamental task for computational drug and …

Molecular representation learning contributes to multiple downstream tasks such as
molecular property prediction and drug design. To properly represent molecules, graph …

Recent development of spatial transcriptomics (ST) is capable of associating spatial
information at different spots in the tissue section with RNA abundance of cells within each …

Abstract Molecular Property Prediction (MPP) is a crucial task in the AI-driven Drug
Discovery (AIDD) pipeline, which has recently gained considerable attention thanks to …

Molecule design is a fundamental problem in molecular science and has critical applications
in a variety of areas, such as drug discovery, material science, etc. However, due to the large …