Science generates data
faster than it can read it.
Mass spectrometry produces millions of spectra daily across pharmaceutical research, clinical labs, and environmental science. Each spectrum is a molecular fingerprint — but reading them requires expert analysts, weeks of manual work, and expensive tooling.
The bottleneck is not data collection. It is interpretation. The scientific community has built enormous datasets but lacks the infrastructure to make them searchable, comparable, and learnable at scale.
* Projections based on market R&D — pre-commercial stage
NexaMol —
Scientific
Foundation
Models
A foundation model trained on the GeMS v1 corpus — 579 GiB of ML-ready mass spectra — learning the underlying language of molecular fragmentation for instant retrieval and structural inference.
Upload
Submit any spectrum via API — instrument-agnostic, format-flexible.
Retrieve
Nearest-neighbor search across the full GeMS corpus in milliseconds.
Infer
Structural signals, metabolite candidates, and confidence scores.
Infrastructure first.
Proof before scale.
We target CROs first — the organizations that feel the MS/MS bottleneck most acutely. Small, well-scoped pilots measured on concrete metrics. API-level integration into existing pipelines. No UI disruption.
Make scientific data
universally interpretable.
Not another analytics tool. The infrastructure layer that makes decades of accumulated scientific data searchable, comparable, and learnable — starting with mass spectrometry, expanding to the full spectrum of molecular science.