Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Traditional drug design requires a great amount of research time and developmental
expense. Booming computational approaches, including computational biology, computer …

Rich data and powerful machine learning models allow us to design drugs for a specific
protein target\textit {in silico}. Recently, the inclusion of 3D structures during targeted drug …

Most molecular generative models based on artificial intelligence for de novo drug design
are ligand-centric and do not consider the detailed three-dimensional geometries of protein …

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with
high affinity and specificity to pre-determined protein targets. In this paper, we formulate …

As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

This paper targets at improving the generalizability of hypergraph neural networks in the low-
label regime, through applying the contrastive learning approach from images/graphs (we …

Denoising diffusion models embody a type of generative artificial intelligence that can be
applied in computer vision, natural language processing and bioinformatics. In this Review …

Generating molecules that bind to specific proteins is an important but challenging task in
drug discovery. Previous works usually generate atoms in an auto-regressive way, where …