A DaaS Case Study: From Data to Discovery – How Patsnap Empowers Biopharma Innovation
Biopharma innovation is entering a new era.
With AI now powering everything from target discovery to molecule design, the quality of data behind these models has become the new competitive differentiator. Yet for many teams, progress stalls due to one persistent problem: fragmented, biased, and unstructured public data that undermines the very models designed to accelerate discovery.
A recent case study highlights how a leading AI driven biopharma company overcame these limitations by turning to Patsnap for high quality, preclinical to patent linked datasets.
The results show a powerful truth:
When teams gain access to better data, they make better decisions at greater speed and scale.
The Challenge: When Data Becomes a Bottleneck
The biopharma company set out with an ambitious goal to transform RNA target identification and drug design using advanced AI models.
But three major barriers blocked their progress:
1. High stakes decisions
Every target decision carried huge consequences. A single false positive could cost more than $25M in downstream development.
2. Data gaps and bias
The team relied heavily on public data sources that were inconsistent, unstructured, and incomplete. This slowed discovery, reduced model accuracy, and made it difficult to scale their research workflows.
3. Scalability limits
Their AI models needed richer, timelier biological and patent data to unlock deeper insights at scale. Without this, the models struggled to reach their full potential.
The Solution: Patsnap’s Connected Data Foundation
To break through these constraints, the company partnered with Patsnap to access curated preclinical to patent linked datasets and high-performance retrieval tools.
The Impact: Faster Decisions, Better Predictions, Stronger ROI
The results were immediate and measurable.
They found that with Patsnap’s proven data solution, they managed to achieve:
Greater accuracy in target identification
Validated datasets helped reduce false positives and strengthened confidence in high stakes decisions.
Significant time savings
Up to 70 percent of manual data verification processes were automated, freeing scientists to focus on discovery instead of data cleaning.
Faster integration and scalability
APIs enabled seamless mapping of targets to indications, providing more speed and scalability than in house systems.
Reduced downstream R&D waste
With stronger data and clearer insights, decisions that influence $25M or more in potential spend became far more efficient and reliable.
Instant modeling and visualization
Patsnap connected directly into internal data pipelines, enabling immediate use in modeling, tracking, and decision support tools.
These improvements translated into faster discovery cycles and more informed scientific and strategic choices.
Why It Matters: Better Data Leads to Better Science
As one of the company’s principal scientists put it:
So much of the biological data we need is buried in patents, making it hard to access the insights we need. Patsnap gives us the tools and access to uncover that data, helping us understand disease mechanisms and make better decisions.
When AI is fueled by complete, clean, and connected datasets, biopharma teams can push the boundaries of what is scientifically possible.
The right data foundation accelerates discovery, reduces risk, and enables breakthroughs that would otherwise never reach the patients who need them.
Book a demo today and witness what Patsnap’s data solutions can do for your organization.
