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:
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Scientists spent weeks manually searching 38 million files. Agents now finish in minutes, saving 43,000 hours.
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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
Manual estimates risked $300k units not fitting. Teams now simulate layouts in a digital twin, verifying fit before physical install.
Scientists spent weeks manually searching 38 million files. Agents now finish in minutes, saving 43,000 hours.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Scientists spent weeks manually searching 38 million files. Agents now finish in minutes, saving 43,000 hours.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Agencies slowed localization for 90 markets. Now, AI drafts marketing copy, and native speakers verify clinical precision.
Flying experts to film locations bottlenecked updates. Staff now use AI avatars to turn text into compliant videos without reshoots.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Moderation couldn't keep pace with 600M users. AI agents now filter toxicity while models recognize 2.5B objects to refine search.