AI-driven discovery
Computational design can generate candidates faster than a team can test them experimentally. For decisions that depend on binding measurements, that gap delays candidate selection and feedback into the next design cycle. Pavonis uses VICA™ to compare selected physical peptides, proteins and antibodies, returning measurements your team can use to shortlist candidates or inform another design cycle.
Service study
Selected physical peptides, proteins or antibodies and the target interactions to investigate
Candidate binding in the agreed VICA™ assays, including specificity, affinity and kinetics where supported
Measurements to shortlist candidates or inform another design cycle
Pavonis reviews the samples and conditions before proposing the assays. Molecule design and assessment of therapeutic function remain separate activities in the broader research program.
Customer work
9Bio uses experimental measurements of engineered antibodies to inform its AI-driven optimization program. Its traditional binding systems required specialist equipment and trained operators, adding time and cost to the work.
Through VICA™, Pavonis provided binding validation, pH testing and kinetics. The studies delivered the analysis 9Bio needed at lower cost and with more flexibility, reducing the equipment and training barriers to experimental testing. With those barriers reduced, the work helped accelerate 9Bio’s discovery program.
Candidate comparison
Producing enough material to characterize every designed candidate can consume resources before its binding has been assessed. A small-sample comparison under the same conditions helps your team identify which candidates merit that further investment. Therapeutic efficacy requires separate investigation.
Association (kon) and dissociation (koff) rates can distinguish candidates with similar endpoint responses. Alongside binding affinity (KD) and specificity studies, these measurements help compare candidates where the assay supports them.
If binding changes with the experimental environment, selected solid-phase, solution-phase or matrix studies can help investigate the difference. The comparisons proposed by Pavonis depend on the interaction, molecules and material available.
Example data
The VICA™ Example Data Package presents anonymized assay examples, including association and dissociation rates, affinity values and model-fit information. Together, they show how the readouts relate to different assay formats. When planning your study, Pavonis specifies which of those measurements are suitable for the candidates and samples.
Testing as a service
A team focused on computational design may need to acquire instruments and arrange specialist laboratory testing before it can test its candidates. Pavonis performs the VICA™ experiments as a service, providing binding data without your team having to establish that assay capability in-house. The study requires physical molecules or specimens, so we agree on sample types and quantities before work begins.
VICA™’s manual operation also affects how many candidates can be tested and how much binding analysis is practical in a study. Those requirements are agreed individually; automated thousand-compound screening and specialist low-picomolar measurements are outside the platform’s current capabilities.
Describe the candidate set, target and measurements needed for your next design or selection decision, including any conditions you want to compare. With those requirements and the material available, we can propose a study that gives your team experimental binding data to guide its next round of work.
