Andrew Viterbi, the legendary Qualcomm co-founder and inventor of the Viterbi Algorithm, has committed $5 million to establish an endowed chair at Sanford Burnham Prebys Medical Discovery Institute, signaling major philanthropic momentum behind AI-powered genomics research. The gift will support the Center for Data Science and Artificial Intelligence, where researchers are developing computational tools to automate genome sequencing analysis and unlock patterns hidden in vast biological datasets.

The investment arrives at a critical moment in genomics. When scientists sequence a human genome, they discover roughly 3.5 million genetic variants, but only 0.6% of those variants appear in protein-coding regions that researchers understand well. The remaining 99.4% exists in what scientists call the "non-coding genome," a largely mysterious landscape that controls when and where genes turn on and off.

Why Is the Non-Coding Genome So Hard to Decode?

The challenge is staggering in scale. Protein-coding genes represent only about 2% of the entire human genome, yet they are the only part scientists have truly "decoded." The rest is written in what researchers describe as an entirely different language, one that governs gene regulation, tissue-specific expression, and disease susceptibility.

Traditional methods for studying these regulatory regions have been painfully slow. Historically, researchers tested how mutations in individual enhancers or promoters affect nearby genes one at a time, a process that would require centuries to map the entire regulatory landscape. But over the past 15 years, scientists have developed a suite of techniques called massively parallel reporter assays (MPRAs), which allow researchers to test millions of genetic elements simultaneously in a single experiment.

"Whenever we sequence a human individual, we get about 3.5 million variants, and only 0.6% of those will be in coding regions. For the rest, we really don't understand what it's doing," said Nadav Ahituv, a geneticist at the University of California, San Francisco.

Nadav Ahituv, Geneticist at University of California, San Francisco

These assays work by linking millions of DNA variants to unique "barcodes," then measuring which variants trigger gene expression. The largest MPRA experiments have tested close to two billion DNA fragments in a single run, revealing regulatory elements that would have been invisible using older methods.

How Is AI Transforming Computational Biology?

The computational biology market is experiencing explosive growth, driven largely by AI and machine learning. The global market was valued at $5.14 billion in 2025 and is projected to reach $14.94 billion by 2034, growing at an annual rate of 12.89%. This acceleration reflects a fundamental shift in how researchers approach biological data.

AI systems are now being trained to identify patterns in genomic data that humans would miss. Machine learning models can rapidly analyze millions of genetic variants, predict which ones affect disease risk, and even help design new therapeutic approaches. Companies and research institutions are increasingly embedding AI directly into their scientific software platforms rather than using separate, disconnected tools.

The inaugural holder of Viterbi's endowed chair will be Yuk-Lap (Kevin) Yip, director of the Center for Data Science and Artificial Intelligence at Sanford Burnham Prebys. Dr. Yip is recognized globally for using advanced analytics and machine learning to uncover insights from complex biological data, accelerating progress in disease research and precision medicine.

Steps to Advance AI-Driven Genomics Research

• Standardize Genome Analysis: Develop automated, standardized computational tools that enable scientists to decipher entire genomes of patient samples, animal models, and cultured cells in single experiments, reducing manual work and human error.
• Integrate Multi-Omics Data: Combine genomic, proteomic, and transcriptomic datasets into unified platforms so researchers can analyze how genes, proteins, and RNA interact across tissues and disease states.
• Train AI Models on Regulatory Elements: Use massively parallel reporter assays to generate large-scale datasets of how millions of DNA sequences regulate gene expression, then train machine learning models to predict regulatory function and design synthetic genetic circuits.

The regulatory genome holds enormous practical implications for medicine. Customized regulatory elements could tighten control of gene-based therapies, ensuring treatments activate only in specific tissues and under particular conditions, minimizing off-target effects. This precision is critical for therapies that could otherwise cause harm if they activate in the wrong place or time.

Viterbi's background makes his investment particularly symbolic. He is renowned for inventing the Viterbi Algorithm, a dynamic programming method that finds the most likely sequence of hidden events that would explain a sequence of observed events. That same principle underlies modern AI approaches to genomics, where machine learning models must infer hidden regulatory logic from observable genetic variation.

"I hold deep confidence in the strength of the leadership at Sanford Burnham Prebys to steward this investment with vision and purpose. Biomedical research and collaboration generate momentum used to advance computing and artificial intelligence," said Andrew Viterbi.

Andrew Viterbi, Co-founder of Qualcomm Inc.

The broader computational biology market is being shaped by several converging trends. Precision medicine adoption is accelerating, driving demand for software that can interpret genomic variants, identify disease biomarkers, and stratify patients for targeted therapies. Cloud-based platforms are making it easier for research institutions and hospitals to store and analyze massive genomic datasets without building expensive on-site infrastructure.

However, significant challenges remain. Data fragmentation is a major obstacle; genomic, clinical, and proteomic datasets often live on separate systems using different formats and metadata standards, making integration slow and costly. Additionally, there is a critical shortage of skilled bioinformatics and computational talent, limiting how quickly institutions can scale adoption of new AI-driven tools.

Despite these hurdles, the momentum is unmistakable. Major pharmaceutical companies, research institutes, and diagnostic laboratories are investing heavily in computational biology platforms. In October 2025, Benchling launched Benchling AI, a scientific AI command center designed to bring AI agents and predictive models directly into the platform used by scientists. In April 2025, Illumina announced a collaboration with Tempus to accelerate clinical adoption of next-generation sequencing tests through genomic AI innovation.

Viterbi's $5 million gift represents more than a single donation; it signals that the world's most accomplished technologists recognize genomics as the next frontier for AI innovation. As researchers decode the regulatory grammar of the genome, they will unlock new possibilities for understanding disease, designing therapies, and ultimately, improving human health at a scale and speed once unimaginable.