Target identification is a critical and challenging step in drug discovery, with only a small fraction of human genes considered druggable and even fewer successfully targeted by approved therapies. Traditionally, this process has been slow and complex, but artificial intelligence (AI) is transforming it into a more systematic, data-driven approach. 

A recent review by Insilico Medicine highlights how AI is accelerating target discovery by integrating vast multimodal datasets—including omics data, clinical records, imaging, and scientific literature—to uncover novel disease mechanisms and therapeutic targets. 

Advanced machine learning methods, such as supervised and unsupervised learning, graph neural networks, and generative AI models, enable researchers to prioritize targets, simulate biological systems, and generate new hypotheses with greater precision. Platforms like PandaOmics and emerging “virtual biologist” AI agents further enhance this capability by synthesizing complex biological knowledge. 

Clinical progress demonstrates the real-world impact of these approaches, with AI-enabled platforms accelerating timelines and contributing to promising therapies, such as the TNIK inhibitor rentosertib for idiopathic pulmonary fibrosis. 

Looking ahead, the field is moving toward AI-driven closed-loop systems that integrate computational predictions with automated experimentation, aiming to significantly improve efficiency, success rates, and the delivery of new treatments to patients. 

Chicken eggs are already used to harvest helpful proteins called antibodies to protect humans from viruses such as influenza. Now, a breakthrough at the University of Missouri could one day lead to chickens that produce other useful medical proteins in their eggs.

SYNGAP1 encephalopathy is a rare genetic disorder for which there is no treatment, causing epilepsy, intellectual disability, psychomotor delay and, frequently, autism. It is caused by mutations in the SYNGAP1 gene, which produces a protein essential for brain and cognitive development. Now, a multicentre study describes the wide variability in clinical symptoms among patients and reveals that the severity of the disease does not depend exclusively on the SYNGAP1 gene, but on other genetic factors that may modulate its clinical expression.

A new global study reveals dietary risks caused 5.91 million cardiovascular deaths in 2023. High sodium, low fruit, and low whole grain intake are the primary drivers. Although per-capita mortality is declining, aging populations are causing absolute death numbers to rise, particularly in China and Pacific Island nations.

New research has highlighted how inadequate environmental regulations and assessments of contaminated industrial land in India are endangering human populations and wildlife ecosystems.

Salk researchers collaborated with scientists at Albert Einstein College of Medicine to develop a new class of probes for imaging living cells. The probes, called visible-spectrum antigen-stabilizable fluorescent nanobodies (VIS-Fbs), generate high-contrast images with minimal disruption to normal cellular activity. The technology enables more precise investigation of complex biological processes, including cell signaling, development, and disease progression.