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Pioneering the future of proteomics with AI-powered insights from raw mass-spectrometry data

Potential impact

Proteomics: Bridging Genes to Phenotypes

Proteomics and mass spectrometry data is instrumental in discovery efforts but can be challenging to interpret


In multiple multi-modal biomarker discovery efforts (in various indications), plasma or tissue proteomics comes up as the most informative data type

Core Component

Unlike genomics and transcriptomics that represent cellular blueprint, proteins are the main functional units that carry out cellular functions


Proteomics with mass spectrometry can be used in many different applications, from target identification, drug discovery, to outcome prediction

Current Problem

Proteomics and mass spectrometry data is instrumental in discovery efforts but can be challenging to interpret

Imprecise quantification

Intensities of low-abundance proteins can be off by orders of magnitude.

Slow computation

Current tools are designed to work on workstations with limited compute power, creating significant bottlenecks in labs.

High false discovery

Searching highly complex samples further reduces the identification rate

Incomplete and inaccurate protein identification

Existing workstation-based software are comprised of algorithms that often miss out on identifying proteins or analytes in a sample.


Advanced deep learning approaches dramatically increase insight generation by leveraging external datasets to amplify insights from localized datasets

How it works

Tesorai’s early AI model significantly increases accuracy of processing proteomics data compared to current approaches

Significant increase in peptide-spectrum matches*

* evaluated on a left-out organism and PRIDE project (Arabidopsis thaliana)

Massive reduction in false-negative identification*

* This evaluation is performed on the ProteomeTools dataset (a set of chemically synthesized peptides)
** This rate represents an identification rate of 92%

Our process

Easy to use

We thrive to build the most accurate and the easiest to use data pre-processing workflows
Drag-n-drop your raw file(s)
Download the results
Integrate seamlessly with analysis pipelines

Test with your sample

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