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 …

Deep generative models have gained recent popularity for chemical design. Many of these
models have historically operated in 2D space; however, more recently explicit 3D …

Fragment-based drug discovery has been an effective paradigm in early-stage drug
development. An open challenge in this area is designing linkers between disconnected …

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 …

Recently, deep generative models have been regarded as promising tools in fragment-
based drug design (FBDD). Despite the growing interest in these models, they still face …

Drug discovery is a time-consuming and expensive process. With the development of
Artificial Intelligence (AI) techniques, molecular Geometric Deep Learning (GDL) has …

Designing 3D ligands within a target binding site is a fundamental task in drug discovery.
Existing structured-based drug design methods treat all ligand atoms equally, which ignores …

Designing compounds with desired properties is a key element of the drug discovery
process. However, measuring progress in the field has been challenging due to the lack of …

Targeted protein degradation techniques, such as PROteolysis TArgeting Chimeras
(PROTACs), have emerged as powerful tools for selectively removing disease-causing …

Diffusion models, a powerful and universal generative artificial intelligence technology, have
achieved tremendous success and opened up new possibilities in diverse applications. In …