Highlights•Chemical graph theory and descriptors in drug discovery.•Chemical fingerprint
and similarity analysis.•Machine learning models for virtual screening.•Future challenges …

There is a continued desire in biomedical research to reduce the number and duration of
design cycles required to optimize lead compounds into high-quality chemical probes or …

The pharmaceutical industry remains solely reliant on synthetic chemistry methodology to
prepare compounds for small‐molecule drug discovery programmes. The importance of the …

Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that
protects cells. These peptides share common structural and physicochemical properties and …

This review is focused on several machine learning approaches used in chemoinformatics.
Machine learning approaches provide tools and algorithms to improve drug discovery. Many …

Activating mutations in Gαq proteins, which form the α subunit of certain heterotrimeric G
proteins, drive uveal melanoma oncogenesis by triggering multiple downstream signaling …

The ability to efficiently synthesize desired compounds can be a limiting factor for chemical
space exploration in drug discovery. This ability is conditioned not only by the existence of …

The US Environmental Protection Agency is developing chemical screening and
prioritization programs to evaluate environmental chemicals for potential risk to human …

High-throughput screening (HTS) is an integral part of early drug discovery. Herein, we
focused on those small molecules in a screening collection that have never shown …

The development of DNA-encoded library (DEL) technology introduced new challenges for
the analysis of chemical libraries. It is often useful to consider a chemical library as a stand …