Despite recent interest in deep generative models for scaffold elaboration, their applicability
to fragment-to-lead campaigns has so far been limited. This is primarily due to their inability …

Machine learning has been increasingly applied to the field of computer-aided drug
discovery in recent years, leading to notable advances in binding-affinity prediction, virtual …

Monkeypox is a serious public health issue in tropical and subtropical areas. Antivirals that
target monkeypox proteins might lead to more effective and efficient therapy. The F13 …

Introduction Scaffold hopping has emerged as a practical tactic to enrich the synthetic bank
of small molecule antitumor agents. Specifically, it enables the chemist to refine the lead …

In silico assessment of protein receptor interactions with small ligands is now part of the
standard pipeline for drug discovery, and numerous tools and protocols have been …

The idea of using deep-learning-based molecular generation to accelerate discovery of drug
candidates has attracted extraordinary attention, and many deep generative models have …

Fragment-based drug design uses data about where, and how strongly, small chemical
fragments bind to proteins, to assemble new drug molecules. Over the past decade, we have …

In early-stage drug discovery, the hit-to-lead optimization (or “hit expansion”) stage entails
starting from a newly identified active compound and improving its potency or other …

The main protease (Mpro) of SARS‐COV‐2 plays a vital role in the viral life cycle and
pathogenicity. Due to its specific attributes, this 3‐chymotrypsin like protease can be a …

Understanding which chemical modifications can be made to known ligands is a key aspect
of structure-based drug design and one that was pioneered by the software GRID. We …