From Data to Discovery: How Patsnap Empowers Biopharma Innovation
Unlock faster, smarter, and more confident drug discovery with AI-powered biopharma intelligence.
An AI-driven biopharma company set out to transform RNA target identification and drug design using advanced computational models. However, fragmented, biased, and unstructured public datasets slowed their progress, limiting model accuracy, scalability, and discovery speed. To overcome these barriers, the company turned to PatSnap’s high-quality, preclinical-to-patent data solutions to fuel their AI innovation pipeline.
Through a collaborative, two-phase approach, PatSnap enabled the client to integrate curated datasets and develop an RAG (retrieval-augmented generation) pipeline that connected drug, sequence, target, gene, and patent data. This allowed for advanced LLM training and RNA efficacy prediction with unprecedented precision.
Key Outcomes:
- 70% time savings through automation of manual data checks.
- Validated datasets, reducing costly false positives in target identification.
- Scalable AI integration via plug-and-play APIs, enhancing research speed and accuracy.
- $25M+ downstream development savings, thanks to improved decision-making and reduced R&D waste.
By transforming fragmented data into connected intelligence, PatSnap empowered the company to accelerate discovery, improve ROI, and drive innovation from lab to market.
Download the case study to see how PatSnap helps biopharma innovators turn complex data into
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