Abstract Structure-based drug design (SBDD) aims to design small-molecule ligands that
bind with high affinity and specificity to pre-determined protein targets. Generative SBDD …

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 …

Drug development is expensive, time-consuming, and has a high failure rate. In recent
years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery …

Drug development is a complex and time-consuming endeavor that traditionally relies on the
experience of drug developers and trial-and-error experimentation. The advent of artificial …

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on
modern drug discovery, but there is still an urgent need for user-friendly interfaces that …

3D structure-based molecular generation is a successful application of generative AI in drug
discovery. Most earlier models follow an atom-wise paradigm, generating molecules with …

Three-dimensional (3D) molecular generation models employ deep neural networks to
simultaneously generate both topological representation and molecular conformations. Due …

Designing protein-binding proteins is critical for drug discovery. However, artificial-
intelligence-based design of such proteins is challenging due to the complexity of protein …

Deep learning-based de novo molecular design has recently gained significant attention.
While numerous DL-based generative models have been successfully developed for …

Despite the significant potential of generative models, low synthesizability of many
generated molecules limits their real-world applications. In response to this issue, we …