Chemical reactions serve as foundational building blocks for organic chemistry and drug
design. In the era of large AI models, data-driven approaches have emerged to innovate the …

Deep learning methods have been considered promising for accelerating molecular
screening in drug discovery and material design. Due to the limited availability of labelled …

The precise prediction of molecular properties is essential for advancements in drug
development, particularly in virtual screening and compound optimization. The recent …

Extracting expressive molecular features is essential for molecular property prediction.
Sequence-based representation is a common representation of molecules, which ignores …

Polymers are high-molecular-weight compounds constructed by the covalent bonding of
numerous identical or similar monomers so that their 3D structures are complex yet exhibit …

Recent years have seen a rapid growth of machine learning in cheminformatics problems. In
order to tackle the problem of insufficient training data in reality, more and more researchers …

Graphs are ubiquitous for modeling complex relationships between objects across various
fields. Graph neural networks (GNNs) have become a mainstream technique for graph …

Molecular representation learning is fundamental for many drug related applications. Most
existing molecular pre-training models are limited in using single molecular modality, either …

Molecular property prediction stands as a cornerstone task in AI-driven drug design and
discovery, wherein the atoms within a molecule serve as nodes, collectively forming a graph …

Biomedical research increasingly relies on the analysis of complex interactions between
biological entities, such as genes, proteins, and drugs. Although advancements in …