Drug developers grapple with the challenge of managing complex and dispersed data, leading to delays, diverted focus, and hindered innovation. While large language models offer potential time-saving benefits, concerns about accuracy and reliability in drug discovery necessitate a solution that combines manual gold-standard practices with advanced generative AI. Prism.bio combines cutting-edge generative AI and traditional academic metascience to create a fast generative AI software platform rooted in rigorous meta-scientific methodologies outlined in numerous peer-reviewed publications. They have a proven track record of deploying AI products successfully for leading pharmaceutical companies, resulting in up to 100x productivity gains, setting them apart from theoretical approaches. Their highly flexible data platform supports both private internal data and public data, making them a versatile solution provider for clients throughout the drug development cycle.
What is the problem?
Drug developers face a daunting problem: an overwhelming amount of complex and scattered data across their orgs, with the potential to accelerate or slow progress dependent upon its utilization. Existing tools fail to effectively manage this challenge, leading to manual analyses by data science teams or consultants, or even neglecting crucial analyses altogether. This manual approach results in queues, delays, and diverted focus for pharmaceutical companies. It also imposes a cost on curiosity, exploration, and innovation. Large language models (LLMs) have the potential to dramatically reduce the time needed to needed for these analyses. But are they ready for prime time? LLMs are notorious for making up information and presenting it with perfect confidence. When it comes to drug discovery and development, with patient lives at stake, these kinds of 'hallucinations' are unacceptable. A solution that combines the rigorous approaches of manual gold-standard approaches and cutting-edge generative AI is needed.
What is their solution?
Prism.bio's approach is a blend of cutting-edge generative AI and traditional academic metascience. Pure AI approaches are often not trusted by scientists, while traditional metascience methods are slow and manual. They have combined the best of both worlds, building a fast generative AI software platform that's rooted in rigorous meta-scientific methodologies outlined in 80+ peer-reviewed publications. They have already deployed large language models and AI products successfully for top pharmaceutical companies, achieving up to 100x productivity gains. With a practical deployment track record, they stand out from those operating solely in theory. They purpose-built a highly flexible data platform that can work well with both private internal data and public data they tap into. This has allowed them to be a broad solution provider for clients across the drug development cycle.
