Tangram Therapeutics
Drug discovery
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
- Up to 50x faster target assessment
- 300x increase in data processing volume
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
A biotech startup combating antimicrobial resistance, where one in three antibiotics is ineffective in animal farming and traditional drug development takes ten years.
Finding the right virus to neutralize specific bacteria required manually screening trillions of potential combinations. This exponential complexity...
“With Amazon SageMaker AI, we can create and run gen AI models that rapidly match phages to each target bacteria, which takes phage therapy from a manual trial-and-error process to precision medicine at scale.”
AI-powered phage therapy platform for sustainable bacterial treatment in livestock.
Cloud computing platform and on-demand infrastructure services.
Phagos's Drug discovery is part of this use case:
Related implementations across industries and use cases
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Manual coaching bottlenecked reps. An AI bot now simulates doctor visits in 14 scenarios, saving 7,000 hours annually.
Scarce specialists bottlenecked care. AI now generates voice therapy that adapts tone and pacing to every patient's symptoms.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
Every HCP response required manually searching regulated literature. Now agents surface cited answers from 3,100+ medical documents.
Every update meant re-recording across five teams. Now a script upload produces multilingual medical video without a single studio session.
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.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
A biotech startup combating antimicrobial resistance, where one in three antibiotics is ineffective in animal farming and traditional drug development takes ten years.
Finding the right virus to neutralize specific bacteria required manually screening trillions of potential combinations. This exponential complexity...
“With Amazon SageMaker AI, we can create and run gen AI models that rapidly match phages to each target bacteria, which takes phage therapy from a manual trial-and-error process to precision medicine at scale.”
AI-powered phage therapy platform for sustainable bacterial treatment in livestock.
Cloud computing platform and on-demand infrastructure services.
Phagos's Drug discovery is part of this use case:
Related implementations across industries and use cases
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Manual coaching bottlenecked reps. An AI bot now simulates doctor visits in 14 scenarios, saving 7,000 hours annually.
Scarce specialists bottlenecked care. AI now generates voice therapy that adapts tone and pacing to every patient's symptoms.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Medicinal chemists had no way into binding simulations—that was computational chemistry's territory. AI gave them a browser and a button.
Every HCP response required manually searching regulated literature. Now agents surface cited answers from 3,100+ medical documents.
Every update meant re-recording across five teams. Now a script upload produces multilingual medical video without a single studio session.
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.