High Throughput Screening (HTS) platform

Selvita offers custom assay development and screening/profiling services up to 1536-well plate format.  We have created a robust compound management infrastructure supporting manipulation of samples in racks, tubes and plates.  We employ a fully automated HTS platform allowing testing of 10,000 compounds a day across a wide range of biochemical and cellular assay types.  We operate HCS screening in support of client projects within our new state of the art HCS laboratory and are generating novel machine-learning based models to support enhanced image analysis in the cell types of particular interest to our clients.

Selvita has initiated the design and building of a new HTS library which will be grown and developed further to provide an excellent resource for hit identification. This compound collection is supplemented by the FideltaMacro™ library with over 1300 diverse macrocyclic molecules.

Our team is built on extensive expertise in the areas of molecular biology, biochemistry, immunology and cell biology. We have a track of record in assay development for various classes of targets, including enzymes (kinases, phosphatases, metabolic enzymes, immuno-modulatory enzymes) and protein receptors (GPCRs, nuclear receptors). Selvita’s state of art-equipment allows for a wide variety of detection modes to be implemented, adding value to your drug discovery program.

Biochemical and cell-based assays

Supported readouts:

  • Absorbance and Fluorescence (endpoint, kinetic)
  • Fluorescence Resonance Energy Transfer (FRET) and Time -Resolved FRET (TR FRET)
  • Fluorescence Polarization (FP)
  • Luminescence
  • Scintillation counting (3H and 14C)
  • Cell based reporter & viability assays
  • AlphaScreen bead-based proximity assays

Biophysical assays

  • Surface plasmon resonance (SPR)
  • Thermal shift (differential scanning fluorimetry)
  • Microscale thermophoresis (MST)
  • Isothermal titration calorimetry (ITC)
  • Protein-ligand NMR

Selvita can offer case-by-case selection of suitable cellular model/s to investigate the biological mechanism of interest (e.g. cancer cell panels of a specific type and/or with common mutation patterns). Our in-house capabilities for reagent generation include in-house production and validation of recombinant cell lines and recombinant proteins. Our assay development strategies are characterized by efficient and effective protocols. Usually, multi-factor design of experiments is employed during method development.  It accelerates optimization of the assay performance, thus helping to obtain a measurement system that meets its requirements. Statistical parameters are used to support a reliable and robust assay for routine use, e.g. IC50/EC50; mRSD (mean Relative Standard Deviation); S/N ratio; Z’ factor; plate uniformity.


  • Based on Beckman (Labcyte)Access Workstation​
  • From sample-to-readout system​
  • Plate density up to 1536-well​
  • Throughput up to 10 000 cpds/day​
  • Single point and dose-response mode


  • Acoustic dispenser Echo650​
  • CERTUS FLEX reagent dispenser​
  • PHERAstar FSX multimode plate reader



  • Binding studies 
    (Fluorescence Polarisation, HTRF®)​
  • Enzymatic inhibition 
    (e.g. LanthaScreenTM, ​
    TR-FRET based assays)​
  • Phosphorylation studies 
    (AlphaLISA®, HTRF®)​
  • Protease assays 


  • Protein receptors (e.g. GPCR) –​
    binding and cAMP/IP level (HTRF®)​
  • Binding in living cells 
  • Cytotoxicity/proliferation 
    (luminescence, fluorescence, absorbance)​
  • Intra- and extracellular metabolites
  • Protein biomarkers
    (AlphaLISA®, HTRF®)​

Iterative Chemical Space Exploration

We offer a unique approach for building custom screening libraries by using Artificial Intelligence-enhanced chemical space exploration. This approach allows us to focus our search on those regions of chem space that are relevant to a given biological target, which increases our odds of finding good hits.

  • A subset of our diverse, high-quality lead-like compound library (around 25 000 compounds) is screened to obtain initial data​
  • An AI model is trained on the data to generate enriched compound set for another round of screening​
  • The process is repeated several times to yield an improved model (reusable in the future!) and a more enriched library

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