Sunny Ray talks with Robin Park, CEO of Chaparral Labs, about where AI can make a real difference in drug discovery and why data remains one of the field's biggest bottlenecks. Park traces his path from computer science into bioinformatics and proteomics, including about 16 years at Scripps Research and the founding of a company later acquired by Rooker in 2020. He explains why instruments keep producing more data while interpretation lags behind, and uses a smoke and fire analogy to separate biomarkers from true drug targets. Park describes Chaparral's phased plan: a data analysis platform already in production with about 10 paying academic and industry customers, followed by an AI layer that builds project-specific knowledge bases and ranks biomarkers and targets, and then a wider rollout to biotech and biopharma companies. He also covers how customers have arrived through word of mouth, the company's near-term milestones, its fundraising plans, and what he wants investors to understand. The conversation closes with how labs can reach him and his goal of helping more patients benefit.
Robin Park of Chaparral Labs explains why data, not algorithms, is the real bottleneck in AI drug discovery, and how to tell smoke from fire.
What problem is Chaparral Labs solving?
I'm the CEO of Chaparral Labs, and we build software and an AI platform that helps scientists turn complex biological data into faster, more reliable drug discovery decisions. Scientists can generate enormous amounts of data, but the hard part is knowing what it means and what decision to make next. That interpretation gap is what we focus on.
When did the idea that instruments get faster but interpretation doesn't become a company for you?
Instruments kept producing more data, but more data did not automatically mean more biological understanding. In fact, larger datasets made interpretation harder. I saw this at my previous company and later inside an instrument company. Hardware improves quickly, but interpretation needs software, biology and deep domain knowledge. Advances in AI then created the opportunity to launch Chaparral Labs.
Can you explain the difference between a biomarker and a drug target for a non-scientist?
Think of smoke and fire. A biomarker is like smoke. It tells you something is happening in the body. A drug target is like the fire. If you change it, you may actually change the disease. Removing the smoke doesn't put out the fire, so we try to separate those two questions early, before scientists spend time and money pursuing the wrong targets.
How is your approach different from AI drug discovery platforms that just read public papers?
For us it starts with data quality and deep domain knowledge, which can't be built overnight. Before adding AI, we spent about two years building the data platform itself. It takes raw data, runs proprietary quality control, and turns it into biologically meaningful information. Garbage in, garbage out applies especially to AI, and even a sophisticated platform can't interpret poor input data.
What are the phases of your roadmap and where are you today?
Phase one is complete. Our data analysis platform launched last year and is in production with about 10 paying customers across industry and academia. In phase two, we're building an AI-powered, project-specific platform where customers combine private data with public evidence, and we rank biomarkers and targets. Phase three is scaling across biotech and biopharma companies.
How did your first customers find you?
Mostly word of mouth and professional relationships. I've spent many years in proteomics and bioinformatics, at Scripps Research and in my previous company, so people already knew my work and trusted our technical expertise. We haven't run formal sales or marketing yet. Customers come with problems, we listen, and tools we build for one often become useful for others.
What are your next milestones and your funding plans?
Over the next 12 to 18 months, we'll keep building and validating the AI platform with real projects with academic and industry collaborators. Then we'll expand those partnerships into broader enterprise adoption with strong case studies. We're looking at fundraising to accelerate development and validation, though revenue lets us keep going without it.
What's the best way for a biotech or academic lab to get started with Chaparral Labs?
They can reach out to me directly on LinkedIn or by email. It doesn't have to be about licensing. I'm always open to hearing what problems people have and how I can help, and I like to collaborate with groups out there. Our ultimate goal is helping scientists find drug targets faster so more patients benefit.
Building something daring? Sunny talks to founders like this every day. Fifteen minutes to see if your story belongs on the stage.
Claim your pre-interviewSend a founder here and we will know it was you.