NVIDIA launched its Bioneo agent toolkit at BIO2026, declaring the arrival of agentic science in life sciences.
“Every single life sciences company is going to become an agentic company.”
drug-discovery
12 tracked signals on drug-discovery.
OpenAI launches GPT-Rosalind with enhanced biological reasoning for life sciences research.
ESMfold acts as a protein world model, generating antibodies as an emergent capability without explicit antibody training.
“We didn't design a model for antibodies. We just designed a model that could understand proteins and you kind of get protein design as an emergent property.”
OpenAI-backed Chai Discovery hit $4B valuation as pharma AI tools deals surge
Chai Discovery is treating drug design as a scaling problem, prioritizing model simplicity over complexity
“one of the exciting things we're trying to do at Chai is to make the drug discovery process like a little bit more like engineering”
Drug discovery AI hits scaling walls from data information limits, not model size or compute
“test loss flatlines after 1.5B parameters while training loss continues to drop as you scale, that tells you that your model is limited by the amount of information in your data”
Microsoft unveils improved topological qubit material and a new quantum processing unit, paired with an AI-quantum-HPC stack for scientific discovery.
“We're happy to announce that we develop much better material that gives us enormous improvement in lifetime. And we also built a new quantum processing unit with this”
Databricks and NVIDIA launch Genesis Workbench, a blueprint for GPU-accelerated AI in life sciences.
NVIDIA frames agentic AI and open biology models as moving scientific discovery from human speed to computational speed.
“Discovery no longer waits on us.”
NVIDIA's BioNeMo Agent Toolkit enables building AI scientist agents for life science discovery.
“But science isn’t software engineering. There is no test suite that turns green when a hypothesis is correct; discovery is iterative, uncertain, and grounded in the physical world.”
AI can't cure cancer by reasoning alone; models lack physical-world grounding and must run real experiments.
“They're still going to have to kind of interface with the real world and generate data because we haven't generated enough data to to understand all of the science yet.”
Modern medicine relies on population-based guessing without mechanistic understanding; the goal is individualized, mechanism-driven treatment.
“we're just making guesses. We really have no mechanistic understanding.”