Mapping interactions took years. A custom AI pipeline analyzed 1.7M predictions in 3 months, finding 40,000 high-confidence pairs.
A research laboratory at a prominent medical school aimed to map the human proteome, consisting of 20,000 proteins and potentially one million complex interactions.
Manually determining protein structures previously took years, creating a bottleneck for understanding disease mechanisms. While AI tools like...
“We’ve had so much success with AlphaFold in our area of biology that we’ve sought to expand it beyond the interest of our lab to the problem that almost all biologists think about, which is how proteins need to interact with each other throughout the human body. We're going from a few hundred proteins interacting in a few hundred ways to the scale of the entire proteome: 20,000 proteins interacting in perhaps a million ways.”
Graduate medical school and center for biomedical research.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Harvard Medical School's Protein interaction discovery is part of this use case:
Related implementations across industries and use cases
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Mapping a billion protein interactions took years; GPU acceleration cut it to weeks, letting researchers test more candidates without more lab runs.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Mapping a billion protein interactions took years; GPU acceleration cut it to weeks, letting researchers test more candidates without more lab runs.
A maze of disconnected systems obscured global trial visibility. Now, a digital twin guides AI to automate heavily regulated workflows.
Every HCP response required manually searching regulated literature. Now agents surface cited answers from 3,100+ medical documents.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Mapping interactions took years. A custom AI pipeline analyzed 1.7M predictions in 3 months, finding 40,000 high-confidence pairs.
A research laboratory at a prominent medical school aimed to map the human proteome, consisting of 20,000 proteins and potentially one million complex interactions.
Manually determining protein structures previously took years, creating a bottleneck for understanding disease mechanisms. While AI tools like...
“We’ve had so much success with AlphaFold in our area of biology that we’ve sought to expand it beyond the interest of our lab to the problem that almost all biologists think about, which is how proteins need to interact with each other throughout the human body. We're going from a few hundred proteins interacting in a few hundred ways to the scale of the entire proteome: 20,000 proteins interacting in perhaps a million ways.”
Graduate medical school and center for biomedical research.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Harvard Medical School's Protein interaction discovery is part of this use case:
Related implementations across industries and use cases
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Mapping a billion protein interactions took years; GPU acceleration cut it to weeks, letting researchers test more candidates without more lab runs.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Mapping a billion protein interactions took years; GPU acceleration cut it to weeks, letting researchers test more candidates without more lab runs.
A maze of disconnected systems obscured global trial visibility. Now, a digital twin guides AI to automate heavily regulated workflows.
Every HCP response required manually searching regulated literature. Now agents surface cited answers from 3,100+ medical documents.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.