DIGITAL BIOLOGY
Features
Biohub, DOE, NIH and partners commit $1.8 billion toward open, AI-ready biological data
The commitment combines funding, data, computation and new measurement technology, and Biohub calls it the largest coordinated commitment to generating AI-ready biological data to date. DOE will invest more than $500 million over five years, NIH will coordinate datasets from more than $500 million in prior federal investment, and Google DeepMind, Isomorphic Labs and Meta are collectively investing $300 million.
GSK expands its Chai Discovery collaboration after wet-lab tests of Chai designs
Chai says its models generated designs to GSK targets zero-shot, without target-specific training, and that GSK's internal wet-lab testing found binders to all tested targets. GSK will gain access to Chai's platform, including its protein folding and design models, across its pipeline. GSK's Christopher Austin said the technology complements GSK's internal models, which it continuously retrains against its own wet-lab data.
Google DeepMind's AlphaProtein Novo designs enzymes for reactions nature never evolved
AlphaProtein Novo is a machine-learning pipeline that designs enzymes from first principles instead of starting from natural sequences, and Google DeepMind says it outperforms natural sequence mining on challenging chemistry. It produced new-to-nature nitrene transferases that make the pharmacophore piperidine with unprecedented product selectivity, and enzymes that degrade the environmental toxin DEHP under conditions that denature natural enzymes. It also reached state-of-the-art catalytic efficiencies on two well-studied model reactions.
Multiply Labs raises $75 million Series B to scale robotic biologics manufacturing clusters
The round was led by Patrick Soon-Shiong with NantWorks and brings total capital raised to over $100 million. Multiply Labs says its robotic clusters deliver a 74% reduction in cost per dose and up to 100 times more throughput than manual manufacturing. Pharmaceutical companies own and operate the clusters rather than buying an outsourced service. The company says it will use the capital to expand manufacturing capacity, building toward commercial-scale production from clinical-stage deployments.
Danaher plans an AI-powered autonomous antibody discovery lab at Abcam
Danaher says the lab, expected to operate at scale in early 2027, will combine AI, robotics and technologies from several Danaher operating companies in one connected workflow. The company projects up to 8x faster discovery of affinity reagents and up to a tenfold increase in annual reagent generation. Both are design goals for a lab that has not yet launched.