Better Data

Tesorai Refine

Reducing false discovery in high-throughput assays

Current Problem

High-throughput assays are built for speed/scale, at the cost of high false discovery rates (in some cases over 90%)

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

Types of assays

Affinity purification with mass spectrometry (APMS), covalent fragment screens with mass spectrometry, DNA-encoded libraries, phage displays, small-molecule binding/activity assays, ribosome profiling

False discovery rate

Assays are noisy, resulting in high false-positive rates (for example, only ~5% of interactions detected in APMS are biologically relevant), with likely large false-negative rates too

Challenges with interpreting data

Biases like sampling and detection favor abundant analytes, alongside unspecific interactions and plate/batch effects, impacting analysis. Limited labels hinder standard AI/ML in custom applications.


Relying on intensities measured by the assays alone can be misleading, and can lead to high failure rates


Tesorai is replacing current experiment-constrained manual engineering and simplistic modeling with data integration and deep learning

How it works

Current ranking model improves upon the gold standard ranking algorithm

Identification of High Potential Protein-Protein Interactions (PPI)

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

Identification of High Potential Protein-Protein Interactions (PPI)

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

Assay types that Onyx suite can be applied to

Identification of new Protein-Protein Interactions (PPI)

Affinity purification & mass spectrometry (AP-MS)
Antibody-antigen mapping
Molecular glues validation

Identification of New Protein- Ligand Interactions (PLI)

Hit identification from High-throughput assays, including by improving batch and plate effects
DNA-encoded libraries

Improving properties of individual molecules

Protein stability
Peptide solubility
Small-molecule library design

Proof of work

Custom Fine-Tuned AI Model

We can leverage our foundation models to address unique challenges in analyzing high-throughput assays with proteins, peptides, and/or small molecules.


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