Researchers waded through millions of disconnected papers. Now they query a live map of biology, where every insight is traceable to its experiment.
A pharmaceutical technology company developing a platform to map disease biology for an industry where bringing a single new drug to market averages 13 years and costs over $2.5 billion.
Early architectures using relational databases could not model the networked nature of biology, requiring slow and complex JOIN operations. More...
“You’re dealing with billions of entities and a combinatorial explosion of possible interactions. But more importantly, you’re dealing with relationships that are conditional, contextual, and experimentally defined. Biology only makes sense when those connections are preserved.”
Neuro-symbolic AI platform for drug discovery and disease biology.
Graph database and analytics platform for enterprise data relationships.
BenchSci's Drug discovery research is part of this use case:
Related implementations across industries and use cases
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Scientists spent half a day digging through 50 years of scattered files. Now, an AI assistant retrieves past experiments in minutes.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Generic AI mangled chemistry terms; manual translation took months. Glossary-tuned AI now earns enough trust to publish before human review.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Legacy keyword search failed on typos and vague queries. Now, semantic AI interprets natural language and images to find exact items.
Researchers waded through millions of disconnected papers. Now they query a live map of biology, where every insight is traceable to its experiment.
A pharmaceutical technology company developing a platform to map disease biology for an industry where bringing a single new drug to market averages 13 years and costs over $2.5 billion.
Early architectures using relational databases could not model the networked nature of biology, requiring slow and complex JOIN operations. More...
“You’re dealing with billions of entities and a combinatorial explosion of possible interactions. But more importantly, you’re dealing with relationships that are conditional, contextual, and experimentally defined. Biology only makes sense when those connections are preserved.”
Neuro-symbolic AI platform for drug discovery and disease biology.
Graph database and analytics platform for enterprise data relationships.
BenchSci's Drug discovery research is part of this use case:
Related implementations across industries and use cases
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Scientists spent half a day digging through 50 years of scattered files. Now, an AI assistant retrieves past experiments in minutes.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Physics simulations took weeks to screen libraries. Boltz-2 runs 1,000x faster, cutting this to hours with 2x precision.
Matching phages required screening trillions of options. Gen AI simulates interactions, cutting discovery from 10 years to 2 months.
Generic AI mangled chemistry terms; manual translation took months. Glossary-tuned AI now earns enough trust to publish before human review.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Legacy keyword search failed on typos and vague queries. Now, semantic AI interprets natural language and images to find exact items.