AI-Powered Antibody–Antigen Affinity Discovery and Optimization Platform
Applications
- Academic labs: Low-budget screening of affinity-matured variants
- Biotech firms: Rapid lead optimization before clinical candidate selection
- Diagnostics developers: Enhance sensitivity and selectivity of assay reagents
We bring high-precision antibody optimization to every laboratory, large or small
AI-driven affinity prediction for rapid, scalable discovery campaigns.
- modification of antibody flexible chains guided by AI,
- prediction of the affinity range (Kd, stability) based on structural input,
- identification of key binding residues for epitope mapping,
- stepwise affinity testing of each antibody–antigen pair,
AI
Affinity Prediction and Binding Analysis: Kd, ΔG, and Epitope Mapping
We have developed a biophysical and AI-supported antibody discovery method to dramatically reduce the cost of experiments in the following fields:
Our platform combines computational and ML techniques (similar to free monoclonal antibody discovery and serum-based antibody sequencing). Core features include:
To assess antibody potency, we support:
Binom Labs introduces a high-throughput, AI-powered antibody affinity optimization service that accelerates the engineering of high-affinity binders at a fraction of the cost of conventional lab methods—offering universities and companies a rapid, scalable alternative to hybridoma, phage display, or SPR screening.
- using advanced data science and mass spectrometry-based analysis.
- antibody–antigen interaction,
- peptide–protein interaction,
- protein–protein interaction,
Financial Benefits Comparison:
Parameter:
Starting at $495
$10,000–$20,000
Average Project Cost:
Traditional Lab Methods:
BinomLabs cost:
10–50 variants per round
10–50 variants per round
Throughput:
Minimal human intervention
Lab Resource Commitment:
Scalability:
High personnel and reagents burden
High — fixed overhead
Low — linear cost with scale
Binom Labs AI SolutionPredictive Modeling:
- Use your antibody–antigen structural data in PDB or docking format
- AI Engine: Machine learning predicts binding affinities (Kd, ΔG), residue-wise contributions, and recommends optimal mutations
- High Coverage: Compute hundreds of variants concurrently
- Fast: Outcomes delivered in 7–10 business days
Reduce Antibody Discovery Costs Using Data Science
How We Identify Critical Binding Residues for Antibody Potency
- Entropy Change: TΔS
- Dissociation Constant: Kd
- Enthalpy change: delta (ΔP), J
- The thermal dissociation: Tm
Molecular Energy Landscape
We compute electrostatic and vibrational potential energies for every residue-residue pair:
- Wp: Total electrostatic interaction energy
- Wp₁: Energy of the lower vibrational level
- Wp₂: Energy of the upper vibrational level
Molecular Insights That Drive Antibody Success:
OutPut/Issued Date after calculations:
Each antibody-antigen complex has its own physical parameters that determine the interaction
Main calculated parameters:
Stability Insights with lg(cond(W))
What is lg(cond(W))?
Why It Matters for You?
Quantify Antibody–Antigen Complex Stability Like Never Before
When designing or selecting therapeutic antibodies, one critical question is:
“How stable is the antibody–antigen interaction under real conditions?”
That’s where the logarithmic condition number, lg(cond(W)), becomes a powerful tool.
When designing or selecting therapeutic antibodies, one critical question is:
“How stable is the antibody–antigen interaction under real conditions?”
That’s where the logarithmic condition number, lg(cond(W)), becomes a powerful tool.
In simple terms, lg(cond(W)) measures the numerical stability of the physical system that governs your antibody–antigen complex. It reflects how sensitive the interaction is to small changes—such as point mutations, thermal stress, or structural shifts.
- Low lg(cond(W)) ⇒ Highly stable, predictable complex
- High lg(cond(W)) ⇒ Fragile or unstable interaction, sensitive to noise or perturbation
🔍 Rank Variants by Stability
Use lg(cond(W)) to compare multiple antibody candidates. Spot the most robust ones—before investing in wet-lab validation.
🧬 Guide Mutagenesis and Optimization
Want to make your antibody less prone to unfolding or off-target effects? Our platform helps identify which regions increase instability.
⚖️ Balance Affinity and Stability
Use lg(cond(W)) to compare multiple antibody candidates. Spot the most robust ones—before investing in wet-lab validation.
🧬 Guide Mutagenesis and Optimization
Want to make your antibody less prone to unfolding or off-target effects? Our platform helps identify which regions increase instability.
⚖️ Balance Affinity and Stability
- An antibody may bind strongly—but be structurally fragile. lg(cond(W)) helps ensure you don’t sacrifice long-term viability.
Developed by Binom Labs:
Antibody affinity describes the intensity with which a single antibody molecule binds to its specific epitope in an antigen. This means that under a given concentration of antibody and antigen, a specific number of antigen–antibody complexes are formed. Consequently, antibody affinity is one of the major properties affecting the potency of therapeutic antibodies. Binders with higher affinities may allow lower doses or longer intervals of administration during therapy. Moreover, as antibodies require sophisticated production systems and therapeutic doses, and costs of goods of antibodies are comparably high, a high affinity may affect the commercial success of a therapeutic antibody. The process of in vivo affinity maturation is described as well as strategies for in vitro affinity maturation. Finally, the relation between affinity and efficacy and the determination of antibody affinity are reviewed.
"Affordable antibody engineering for any research lab—powered by AI."
"We bring high-precision antibody optimization to every laboratory, large or small."
"Modify and test antibodies in silico—no special hardware required."
Procedure for finding suitable immunoglobulins
Comparative price analysis
The step-by-step analysis of each antibody modification is shown in the diagram on the left, as high-affinity antibodies are identified that will be transferred to the antibody synthesis engineering lab.
Conclusion: Experimental and engineering departments will receive preliminary information on the composition and structure of high-affinity antibodies to the antigen, eliminating ineffective antibody variants.
Free Antibody Discovery from Structural Models
Fabs+Epitope
Input mutations in Fabs/Epitope
The goal of the AI platform is to replace and reduce the number of intermediate in vitro experiments
InPut Data windows:
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