Data Science & AI Lecture Series
Building and Utilizing Foundation Models for Drug Discovery and Clinical Development
Chase Neumann
Building and Utilizing Foundation Models for Drug Discovery and Clinical Development
| When | Friday, April 10, 2026, 1:30 PM – 2:30 PM (MT) |
|---|---|
| Where | WEB L112 |
Abstract
The journey toward decoding biology at scale began with a focus on high-dimensional cellular morphology. At Recursion, our differentiation centered on deep learning models designed to learn biological representations directly from imaging, enabling predictive inference at a massive scale. By leading multiple cross-functional teams from early-stage discovery through to early clinical development, we demonstrated the power of this “inference-first” philosophy. A primary highlight of these efforts was the RBM39 program, a novel molecular glue degrader discovered entirely through computational inference rather than traditional screening. This success proved that models could identify complex biological mechanisms; however, moving from cellular discovery to comprehensive patient care requires a leap into even higher-dimensional, clinical data.
Valinor represents the next evolution of this mission. We build multimodal clinical foundation models, co-designing data collection and model architecture to maximize signal within and across complex modalities. We have proprietary access to patient cohorts and collaborate with biobanks, clinical trial sites, and academic partners, giving us unique data advantages at scale. Our approach is to first build the best unimodal patient representations across modalities—including DNA, transcriptomics, proteomics, cfDNA, histopathology, and patient reports—and then fuse them.
We have shown that attention-based fusion consistently outperforms unimodal approaches while making the contributions of different modalities interpretable. This enables genuine clinical reasoning: the model can chain evidence across modalities, explain which features drive a prediction, and engage with clinicians in natural language. Ultimately, we envision a virtual patient that reasons over the full spectrum of a patient’s biology the way an expert clinician would, but at a scale and resolution no human can match.
Speaker
Chase Neumann
PhD
I believe the next generation of life-saving medicines won’t just be discovered; they will be engineered at the intersection of biology and machine learning. During my time at Recursion, I operated at the frontier of “AI for Drug Discovery,” translating high-dimensional data into actionable therapeutic programs. I led cross-functional teams through the critical transition from late-stage discovery into early clinical development, ensuring that AI-driven insights were successfully translated into clinical-ready drug candidates.
With a PhD in Translational Medicine from CCLCM at Case Western Reserve University, I bridge the gap between translational oncology and computational innovation. My work has focused on deconstructing complex disease mechanisms and scaling them through automated, industrialized platforms.
I am a passionate advocate for the “TechBio” shift—moving away from serendipity and toward a predictable model of drug discovery to get better medicines to patients, faster. In late 2025, I co-founded Valinor Discovery to bridge the gap in clinical translation using foundation models trained on real-world multi-modal clinical data.
Tags: biology & genomics health & medicine large language models natural language processing
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