At Seer, we believe the next frontier in AI-driven precision medicine will be defined not only by advances in artificial intelligence, but by the richness and quality of the biological data available to power it.
Our contribution begins with expanding what mass spectrometry can measure. Seer’s proprietary Proteograph® technology enables access to molecular information that conventional proteomic workflows may overlook, helping researchers generate deeper, more diverse biological datasets.
Just as advances in sample preparation and molecular analysis have transformed the information obtainable through DNA sequencing, we believe innovations in proteomic data generation can open new possibilities for understanding human biology.
This opportunity goes beyond identifying more proteins. A single Proteograph-enabled mass spectrometry experiment produces multiple layers of information: from protein identifications and peptide measurements to underlying spectral signals that may provide valuable inputs for emerging AI approaches.
We see this as the beginning of an important new chapter for proteomics, one in which the information captured during measurement becomes increasingly valuable for computational discovery.
The Next Frontier for AI Is Better Biological Data
For years, the depth of a proteomics experiment has largely been measured by the number of proteins identified. This remains an important measure of performance, but it tells only part of the story.
Nanoparticle-enabled mass spectrometry generates a far richer set of molecular measurements than those ultimately represented in a protein table. Conventional analysis transforms these measurements into interpretable biological information, but not every potentially informative signal is retained in the final output.
As AI approaches evolve, we believe there is an opportunity to learn from more of this information, including molecular patterns that traditional identification-based analysis may not fully capture.
The question is no longer simply how many proteins we can identify, but how much meaningful biological information we can make available for discovery.
Beyond the Protein List: Three Layers of Proteomic Data
We see three complementary layers of information that could contribute to the future of AI-driven proteomics.
Protein-Level Data: Interpretable Biological Insights
Identified and quantified proteins connect molecular measurements to biological pathways, disease mechanisms, and potential biomarkers. They remain essential for understanding and interpreting disease biology.
Peptide- and Precursor-Level Data: Greater Molecular Resolution
Peptide and precursor measurements provide a more granular view of the proteome, with the potential to reveal sequence-specific variation and molecular differences that may be obscured in protein-level summaries.
Raw Spectral Data: An Expanded Information Landscape
Underlying mass spectrometry signals contain molecular information beyond what is routinely assigned to identified peptides and proteins. Emerging identification-free AI approaches are beginning to explore whether these patterns can reveal additional biological relationships.
Each layer offers distinct possibilities. Together, they invite us to reconsider what constitutes a valuable proteomic dataset in the age of AI.
Seer’s Role: Enabling Richer Proteomic Data at Scale
Seer’s innovation is not the mass spectrometer itself. It is our ability to expand the biological information available for mass spectrometric measurement.
Our Proteograph® Product Suite combines proprietary engineered nanoparticles with automated, standardized processing to make a broader range of molecular species accessible for analysis. When coupled with high-resolution mass spectrometry, this creates differentiated proteomic datasets that conventional workflows may not capture.
The distinction matters. Seer is not simply providing a sample-preparation step; we are enabling a different depth and breadth of biological measurement, with information available across protein, peptide, and spectral levels.
Our technology is supported by more than 100 scientific publications demonstrating its performance and utility across diverse biological applications. Combined with automated processing and reproducible measurements across batches and studies, this established foundation supports the consistent generation of information-rich datasets across large sample populations.
For AI developers and researchers, we believe this combination of expanded biological access, reproducibility, and scale represents an important opportunity: to learn from molecular information that has historically been difficult to capture or utilize with the consistency needed to support AI-driven discovery across large and diverse cohorts.
From Richer Measurements to New Possibilities in Precision Medicine
What might become possible when AI can learn from a broader representation of the proteome?
We see opportunities to uncover previously unrecognized disease-associated patterns, explore molecular mechanisms, improve biomarker discovery, and integrate proteomic information with other biological and clinical datasets.
These possibilities are promising, but important questions remain. Not every molecular signal is informative, and the value of emerging AI approaches must ultimately be established through reproducible biological findings and rigorous validation.
Our perspective is that richer, high-quality measurements create more opportunities to ask these questions—and potentially discover answers that conventional analyses may overlook.
Building the Data Foundation for What’s Next
We believe AI-driven proteomics is entering an important new phase, one in which the information captured and preserved during biological measurement may become as consequential as the models developed to analyze it.
This perspective represents a starting point for Seer. In the coming weeks, we will share more about the underlying data opportunity, the capabilities of Proteograph technology, and the potential applications of richer proteomic measurements in AI-driven discovery and precision medicine.
Our ambition is to help researchers move beyond the limitations of today’s protein tables and explore more of the biological information already present in their experiments.
Explore the Future of AI-Driven Proteomics with Seer
Interested in generating richer proteomic datasets for AI-driven research? Complete the form below to connect with our scientific team to explore opportunities for collaboration and data generation.
Better models begin with better biology. And better biology begins with better evidence.
