Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
A drug discovery biotech with 50 petabytes of biological, chemical, and patient data and one of the world's most powerful life-sciences supercomputers, built to identify which compounds are worth advancing to wet-lab testing.
Predicting how strongly a drug molecule binds to its protein target is critical for compound prioritization, but the only accurate methods were...
“With AI in the loop today, we can get 80% of the value with 40% of the wet lab work, and that ratio will improve going forward.”
AI-driven drug discovery and clinical-stage biotechnology platform.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Recursion's Drug binding prediction is part of this use case:
Related implementations across industries and use cases
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
Navigating 10^1000 variants was impossible. A model designed novel proteins, boosting stem cell marker expression >50x over controls.
Mapping interactions took years. A custom AI pipeline analyzed 1.7M predictions in 3 months, finding 40,000 high-confidence pairs.
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
Navigating 10^1000 variants was impossible. A model designed novel proteins, boosting stem cell marker expression >50x over controls.
Thousands of legacy apps kept workflows scattered across siloed systems. Concierge replaced them—one AI companion every team fully trusts.
Generic AI mangled chemistry terms; manual translation took months. Glossary-tuned AI now earns enough trust to publish before human review.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
A drug discovery biotech with 50 petabytes of biological, chemical, and patient data and one of the world's most powerful life-sciences supercomputers, built to identify which compounds are worth advancing to wet-lab testing.
Predicting how strongly a drug molecule binds to its protein target is critical for compound prioritization, but the only accurate methods were...
“With AI in the loop today, we can get 80% of the value with 40% of the wet lab work, and that ratio will improve going forward.”
AI-driven drug discovery and clinical-stage biotechnology platform.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Recursion's Drug binding prediction is part of this use case:
Related implementations across industries and use cases
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
Navigating 10^1000 variants was impossible. A model designed novel proteins, boosting stem cell marker expression >50x over controls.
Mapping interactions took years. A custom AI pipeline analyzed 1.7M predictions in 3 months, finding 40,000 high-confidence pairs.
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
Navigating 10^1000 variants was impossible. A model designed novel proteins, boosting stem cell marker expression >50x over controls.
Thousands of legacy apps kept workflows scattered across siloed systems. Concierge replaced them—one AI companion every team fully trusts.
Generic AI mangled chemistry terms; manual translation took months. Glossary-tuned AI now earns enough trust to publish before human review.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
A mistranslated word could derail global R&D projects. Now, researchers instantly refine technical papers & communicate seamlessly across languages.