The strongest disease signal may not point to the best drug target. AI could help researchers identify what matters within complex biological networks.
Stability problems can derail promising biologics late in development. Bringing high-throughput testing earlier into discovery could help researchers identify risks before they become costly failures.
AI agents promise to transform drug discovery, but where do they really add value? Dr Eric Ma examines search, model choice, vendor lock-in and why scientific expertise matters more than ever.
A drug can form exactly the complex it was designed to make and still fail to work. Two recently approved induced proximity drugs show why what happens after complex formation – not formation alone – can decide the outcome.
From self-driving laboratories to advanced cellular models, ELRIG Drug Discovery 2026 showcases technologies helping researchers generate better data, evaluate drug candidates and improve early discovery.
Researchers at Nagoya University have identified BCAT1, an enzyme involved in proline supply for collagen synthesis, as a potential target for reducing cardiac fibrosis following myocardial infarction.
Cambridge biotech bit.bio has released ioAstrocytes 2.0, a genetically matched human astrocyte model designed to support functional neuroscience research and earlier assessment of seizure liability and neurotoxicity in drug development.
PacBio and AI drug discovery company TychoBio are generating full-length RNA datasets from more than 10,000 samples to train AI models and accelerate the development of RNA therapies for rare diseases.
As sequencing continues to grow in importance across biotherapeutic discovery and development, where can it make the biggest impact and how can fragmented workflows be overcome?
Five leading cancer researchers from across genomics, proteomics, cell biology, chemical biology and gene therapy dig into what’s driving cancer drug discovery forward and what’s still holding progress back.
AI agents promise to transform drug discovery, but where do they really add value? Dr Eric Ma examines search, model choice, vendor lock-in and why scientific expertise matters more than ever.