The use of artificial intelligence (AI) in drug discovery has shown promise in recent years with a growing number of new compounds moving forward in the pipeline.

Italfarmaco SpA and Iktos SA have entered into a collaboration to develop next-generation histone deacetylase (HDAC) inhibitors for a variety of non-oncological diseases, including diseases affecting the central nervous system.

Enveda Biosciences (Enveda Therapeutics Inc.) has announced a new series B2 financing round of $55 million. The drug discovery and development company uses artificial intelligence (AI)-powered technologies to translate nature into new medicines.

Archetype Therapeutics Inc. has discovered compounds for the treatment of early-stage lung adenocarcinoma by screening billions of compounds from the Enamine REAL Space chemical library and other libraries.

Owkin Inc. has in-licensed OKN-4395 (ACT-1002-4391), a highly selective and potent dual inhibitor of prostanoid receptors EP2 and EP4, from Idorsia Ltd.

U.S. Precision Medicine Inc. has announced plans to use artificial intelligence (AI) technology to support work on its small-molecule drug candidate for cancer.

A group of scientists from Harvard University have observed and reconstructed the human brain at the resolution of the electron microscope, with all its cells, following all the connections between its neurons around a cubic millimeter of a tissue sample. They took 10 years and the data occupies 1.4 petabytes (1,400 terabytes). However, they are already planning a bigger project.

Avicenna Biosciences Inc. has introduced an extension to its machine learning (ML) technology platform to enhance medicinal chemistry and expedite clinical-stage drug discovery.

In one of the biggest startups ever, Xaira Therapeutics has launched with more than $1 billion from investors. The financing, according to BioWorld records, is roughly equivalent to Roivant Sciences Inc.’s $1.1 billion raise in August 2017 and Galderma Inc.’s $1 billion private placement in June 2023. Xaira said it plans to leverage advanced machine learning research and expansive data generation to power new models for developing new therapeutics.

A computational program based on single-cell transcriptome sequencing has identified six types of senescent cells, enabling the design of more precise senolytic drugs. The success of these compounds depends on their ability to recognize senescent cellular patterns and avoid proliferating cells, differentiated cells, or quiescent (temporarily resting) cells.