Technologies

The chemical proteomics technologies behind better drug discovery decisions

Every drug discovery program faces different challenges.

Understanding target engagement and selectivity within a specific enzyme class provides a more in-depth view than broad-spectrum proteomics, while avoiding the narrow focus of a single-target approach.

Omivera combines small-molecule probes with mass spectrometry to generate insights across an entire enzyme class, supporting better-informed drug discovery decisions. Our flagship product CellEKT allows researchers to see compound behaviour across the kinome.

CellEKT visualizes target engagement across multiple kinase families, helping reveal both intended activity and potential off-target interactions in biologically relevant systems.

    Cellular target engagement profiling or Midostaurin

    Animated kinome target engagement radar plot Radial kinome plot showing Combined, HEK293T and MV4-11 target engagement profiles expressed as pIC50.

    Midostaurin was profiled in a live acute myeloid leukemia cell line, MV4-11 cells, and target engagement was determined across 300+ kinases with each engaged kinase illustrated as a spike indicating the pEC50 value as determined across a full dose-response curve.

    Understanding target engagement and selectivity within a specific enzyme class provides a more in-depth view than broad-spectrum proteomics, while avoiding the narrow focus of a single-target approach. Omivera combines small-molecule probes with mass spectrometry to generate insights across an entire enzyme class, supporting better-informed drug discovery decisions. Our flagship product CellEKT allows researchers to see compound behaviour across the kinome. It visualises target engagement across multiple kinase families, helping reveal both intended activity and potential off-target interactions in biologically relevant systems.

      Which chemical proteomics technology is right for your programme?

      Different drug discovery questions require different profiling strategies. Omivera helps you select the most appropriate technology based on your target class, compound modality, desired readout and biological model.

      CellEKT

      Quantify kinase target engagement and selectivity

      Profile kinase inhibitors across biologically relevant systems to support compound selection and optimisation.

      • Single-dose or dose-response profiling
      • Cell and tissue lysates
      • Living cells
      • In vivo models

      Activity-Based Protein Profiling

      Measure functional enzymatic activity

      Use ABPP to profile serine hydrolase inhibitors or investigate host-cell protein impurities.

      • Proteomics-based readouts
      • Gel-based assays
      • Cell and tissue lysates
      • Living cells

      Tailored Chemical Proteomics

      Build the right approach for your target

      Develop a customised chemical proteomics strategy when established profiling methods do not fit your compound or target.

      • Covalent inhibitor profiling
      • Custom probe development
      • Target engagement assay development
      • Fit-for-purpose experimental design

      Every Omivera technology is built on the same principles.

      Endogenous proteins

      Measure proteins in their native biological environment.

      Biologically relevant systems

      Generate data in living cells and complex biological models.

      Mass spectrometry

      Deep coverage across enzyme families with high sensitivity for quantitative analysis.

      Cross-model profiling

      Compare target engagement and activity across biological systems.

      Actionable insights

      Support confident compound prioritization

      Our workflow

      Every project starts with a scientific question. We work with you to define the objective, select the most appropriate chemical proteomics approach, and generate the data needed to support your next development decision.

      Omivera is led by founders with expertise in chemical proteomics, medicinal chemistry and computational biology.