Mapping a billion protein interactions took years; GPU acceleration cut it to weeks, letting researchers test more candidates without more lab runs.
A global agricultural sciences company running R&D programs in seed innovation and crop protection, researching the protein-level mechanisms by which fungal pathogens attack crops and mining a continuously expanding mass-spectrometry library for protective natural product molecules.
With approximately one billion possible host-pathogen protein interactions to evaluate, computationally mapping the full network took years. In...
“Whenever a pathogen is attacking the plant, it deploys proteins, and it’s a battle. At the same time, the plant is deploying its defense mechanisms to kill the pest and ward off the infection—these are the protein interactions we want to map and understand.”
Agricultural science company providing crop protection, seeds, and digital solutions.
Provided algorithmic insights for Corteva's computational metabolomics workflow.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Corteva's Molecular discovery is part of this use case:
Related implementations across industries and use cases
Mapping interactions took years. A custom AI pipeline analyzed 1.7M predictions in 3 months, finding 40,000 high-confidence pairs.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Teams kept rebuilding agents others had already built—no shared standard, no way to find them. A mesh gave every agent a discoverable home.
Mapping interactions took years. A custom AI pipeline analyzed 1.7M predictions in 3 months, finding 40,000 high-confidence pairs.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Manual 12-day checks for EU deforestation rules created risk. Now, an AI traces the supply chain in 3 minutes, ensuring full compliance.
Minor updates required 3.5-hour re-recordings. Now, teams use AI avatars to regenerate training videos from slides in 30 minutes.
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 a billion protein interactions took years; GPU acceleration cut it to weeks, letting researchers test more candidates without more lab runs.
A global agricultural sciences company running R&D programs in seed innovation and crop protection, researching the protein-level mechanisms by which fungal pathogens attack crops and mining a continuously expanding mass-spectrometry library for protective natural product molecules.
With approximately one billion possible host-pathogen protein interactions to evaluate, computationally mapping the full network took years. In...
“Whenever a pathogen is attacking the plant, it deploys proteins, and it’s a battle. At the same time, the plant is deploying its defense mechanisms to kill the pest and ward off the infection—these are the protein interactions we want to map and understand.”
Agricultural science company providing crop protection, seeds, and digital solutions.
Provided algorithmic insights for Corteva's computational metabolomics workflow.
NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.
Corteva's Molecular discovery is part of this use case:
Related implementations across industries and use cases
Mapping interactions took years. A custom AI pipeline analyzed 1.7M predictions in 3 months, finding 40,000 high-confidence pairs.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Teams kept rebuilding agents others had already built—no shared standard, no way to find them. A mesh gave every agent a discoverable home.
Mapping interactions took years. A custom AI pipeline analyzed 1.7M predictions in 3 months, finding 40,000 high-confidence pairs.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Manual 12-day checks for EU deforestation rules created risk. Now, an AI traces the supply chain in 3 minutes, ensuring full compliance.
Minor updates required 3.5-hour re-recordings. Now, teams use AI avatars to regenerate training videos from slides in 30 minutes.
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.