Apply for a Free Pilot Project for Antibody and Protein Research!
We help researchers make more confident decisions before committing to broad wet-lab validation.
Cut Experimental Cost and Time Before Expanding Wet-Lab Testing
Our mission is to enable researchers to make confident decisions from prediction to validation. Reduce experimental iterations by predicting binding affinity, stability, and interaction order behavior before wet-lab validation.
For antibody and protein R&D teams that want to prioritize the next experiment before committing to broad wet-lab testing
Antibody–antigen ranking/ Mutation prioritization/ Pilot report in 14 days
Reduce the number of experimental branches you need to explore — for example, from 10 candidate paths to 4 priority paths for validation.
We help antibody, protein, and biopharma teams prioritize the next experiments most worth validating.
Reduce experimental iterations by predicting: binding affinity, stability, interaction hotspots, and complex-formation behavior before full wet-lab validation.
Predict experimental outcomes for next research fields:
Optimization goals
- Affinity improvement
- Specificity improvement
- Stability improvement
- Reduced aggregation
- Improved solubility
Binding and interaction metrics
- Relative affinity trends
- Kd / kon / koff-related behavior
- Competition and binding-shift tendencies
- Key interaction residues and likely binding regions
Functional and assay-relevant outcomes
- Potency-related trends such as IC50 / EC50 shifts
- Pathway-response tendencies
- Comparative cell-based efficacy hypotheses
Developability-related properties
- Aggregation tendency
- Thermal stability trends
- Solubility-related behavior
- Experimental-condition sensitivity
Better prioritization of variants for validation
How the Pilot Works?
- Antibody discovery and development teams
- Protein interaction and structural biology groups
- Biotech and biopharma R&D teams
- Translational research groups
- Core facilities supporting molecular and biochemical studies
- Early-stage biotech companies exploring AI-assisted prioritization
Simple workflow to start a pilot project:
Who this pilot is for:
1.Define your research question
Tell us what interaction, mutation set, stability problem, or experimental bottleneck you want to study.
2.Share available data
Provide sequences, optional structures, and any relevant experimental context.
3.Receive a focused computational analysis
We generate predicted interaction, affinity, stability, or mutation-prioritization outputs.
4.Compare with experimental validation
Your team reviews the predictions and checks which candidates or conditions are most worth validating.
5.Review the pilot outcome
We summarize the most useful findings, practical limitations, and the next best validation steps.
Tell us what interaction, mutation set, stability problem, or experimental bottleneck you want to study.
2.Share available data
Provide sequences, optional structures, and any relevant experimental context.
3.Receive a focused computational analysis
We generate predicted interaction, affinity, stability, or mutation-prioritization outputs.
4.Compare with experimental validation
Your team reviews the predictions and checks which candidates or conditions are most worth validating.
5.Review the pilot outcome
We summarize the most useful findings, practical limitations, and the next best validation steps.
What decisions it helps improve:
What the Pilot Evaluates:
Antibody–antigen systems
Protein–protein systems
Experimental-condition ranking
- Antibody optimization against a defined antigen
- Mutation prioritization
- Binding-site interpretation
- Affinity-related ranking of candidate variants
Protein–protein systems
- Alanine scanning and mutational analysis
- Identification of key binding residues
- Assembly order of protein complexes
- Comparative stability of complex formation
- Relative changes in Kd, ΔG, and related interaction metrics
Experimental-condition ranking
- Temperature effects
- Salt and buffer effects
- Prioritization of conditions most worth testing experimentally
- Which antibody variants are most worth testing first
- Which mutations are most likely to improve binding or stability
- Ranking of mutations for optimization workflows
- Selection of the most promising antibody or protein variants
- Which residues are most important for the interaction interface
- Identification of likely interaction hotspots and binding residues
- Selection of experimental conditions with the highest expected value
- Which candidate complexes are most plausible or stable
- Reduction of low-probability experimental branches
- Which hypotheses are strong enough to justify wet-lab follow-up
- How to narrow broad experimental spaces into a smaller, better-ranked test plan
Detailed description with diagrams and examples. Examples of advanced features.
Traditional workflow
- generate many variants
- screen by ELISA / SPR / BLI
- run thermostability assays
- iterate through multiple rounds
Reducing Antibody Screening Burden with Affinity and Binding Analysis
BinomLabs: Affinity trend prediction
Helps prioritize candidates before experimental testing
Helps prioritize candidates before experimental testing
BinomLabs: hotspot residue mapping
Focuses mutagenesis on the most relevant sites
Focuses mutagenesis on the most relevant sites
AI-supported workflow
- predict likely high-value variants first
- focus mutagenesis on key residues
- narrow the candidate list before testing
- validate a smaller, better-ranked set experimentally
- Epitope Mapping: Alanine Scanning Mutagenesis,
- peptide-protein interactions
- identification of key binding residues
- prediction of the :
- affinity Kd, IC50% prognosis
- Stability parametr lg(cond(W))
- entropy change T(delta)S
- free energy change (delta)(delta) G
Protein–protein interaction : Kd, ΔG, and Epitope Mapping
- monomers [P1]
- dimers [P1-P1], [P1-P2]
- trimers [P1-P1-P1], [P1-P2-P3]
- protein+inhibitor [P1-Inh]
- dimer+inhibitors [P1-P2-Inh]
- tetramer+DNA
The correlation between calculated and
experimental parameters reaches 93%
experimental parameters reaches 93%
The prediction method is suitable for:
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Start applying for the pilot project! Fill in the fields with the required information about your project. Check the box below the form if you require strict confidentiality. |
