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
Scientists spent weeks manually searching 38 million files. Agents now finish in minutes, saving 43,000 hours.
A biotechnology leader navigates a search space of 10^60 potential small molecules while analyzing 38 million biomedical publications and internal repositories of hundreds of millions of cells.
Scientists spent weeks manually searching these scattered sources to identify drug targets and validate biomarkers. This inefficient process consumed...
“One of the things that I'm especially excited about, through the development of tools such as autonomous agents, is the ability to democratize access to data sets and computational tools to scientists who maybe have less computational background. Through interacting with an agent in a really iterative and coherent way, we’re able to take advantage of these tools and use that to directly accelerate the research. The agent really gives us an unbelievable boost in the work that we're trying to achieve.”
Biotechnology company developing medicines for life-threatening diseases.
Cloud computing platform and on-demand infrastructure services.
Genentech's Drug discovery research 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.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Validating hypotheses took specialists weeks of manual coding. Now, an AI agent drafts analyses for expert review in minutes.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
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.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.
Scientists spent weeks manually searching 38 million files. Agents now finish in minutes, saving 43,000 hours.
A biotechnology leader navigates a search space of 10^60 potential small molecules while analyzing 38 million biomedical publications and internal repositories of hundreds of millions of cells.
Scientists spent weeks manually searching these scattered sources to identify drug targets and validate biomarkers. This inefficient process consumed...
“One of the things that I'm especially excited about, through the development of tools such as autonomous agents, is the ability to democratize access to data sets and computational tools to scientists who maybe have less computational background. Through interacting with an agent in a really iterative and coherent way, we’re able to take advantage of these tools and use that to directly accelerate the research. The agent really gives us an unbelievable boost in the work that we're trying to achieve.”
Biotechnology company developing medicines for life-threatening diseases.
Cloud computing platform and on-demand infrastructure services.
Genentech's Drug discovery research 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.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Validating hypotheses took specialists weeks of manual coding. Now, an AI agent drafts analyses for expert review in minutes.
Target assessments took a full quarter. Now, AI agents synthesize 1,000+ datasets to finish in hours.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
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.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Software updates were tied to rigid vehicle production cycles. A GenAI platform now frees 5,000 developers to release code independently.