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
NexaMol Final_V3 — a 12.38M-parameter encoder-only transformer trained on ~201M spectra from the GeMS v1 corpus. Contrastive loss fell 45.7% across V1–V3. Structure alignment validated on RDKit Morgan fingerprints. Inference layer live with 5M spectra indexed in Qdrant.
Encode
Encoder-only transformer embeds any MS/MS spectrum into a chemically meaningful representation space.
Align
Morgan fingerprint alignment maps embeddings to molecular structure — validated at cosine 0.4255.
Retrieve
Qdrant-backed nearest-neighbor search across 5M indexed spectra. 100K rich chemical-space atlas.
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.