Your AI Antibody Design Partner
Your AI Antibody Design Partner
Accelerate antibody development through AI-powered innovations in molecule design and optimization, creating tomorrow's therapies today.
Accelerate antibody development through AI-powered innovations in molecule design and optimization, creating tomorrow's therapies today.
Generate
Predict
Explain
Predict binding & developability
Generate
Predict
Explain
Predict binding
Supported capabilities
Supported capabilities
01
01
Better leads,
fewer cycles
Optimize affinity, stability, solubility, immunogenicity, and expression in a single AI-driven molecule design pass. Each lead is derived from millions of in silico designs, ranked and filtered to maximize success and cut lab iterations.
Optimize affinity, stability, solubility, immunogenicity, and expression in a single AI-driven molecule design pass. Each lead is derived from millions of in silico designs, ranked and filtered to maximize success and cut lab iterations.
02
02
Prioritize high-performing candidates before entering the lab
ConvergeAB screens millions of antibody sequences per target using AI-driven molecule design, scoring each for affinity, developability, epitope specificity, humanization, and structural fit. Clear scores and visual outputs enable confident selection, accelerating discovery and reducing experimental burden.
ConvergeAB screens millions of antibody sequences per target using AI-driven molecule design, scoring each for affinity, developability, epitope specificity, humanization, and structural fit. Clear scores and visual outputs enable confident selection, accelerating discovery and reducing experimental burden.
03
03
Generate, optimize and screen - all in one single platform
Each candidate includes rich predictive profiles - affinity, stability, solubility, immunogenicity, and docking-based structural fit - powered by AI-driven molecule design. Review key metrics, explore and interact with 3D visualizations, and export top sequences for synthesis. Converge manages wet-lab validation and operational overhead with partner CRO's, helping you move forward with confidence and clarity.
Each candidate includes rich predictive profiles - affinity, stability, solubility, immunogenicity, and docking-based structural fit - powered by AI-driven molecule design. Review key metrics, explore and interact with 3D visualizations, and export top sequences for synthesis. Converge manages wet-lab validation and operational overhead with partner CRO's, helping you move forward with confidence and clarity.
How it works
How it works
ConvergeAB™ is trained on over 1 trillion natural protein tokens, including 7 million antibody sequences, 3 million antibody–antigen pairs, and 100,000 fully characterized antibodies. This foundation enables affinity maturation, developability optimization, humanization, patent escape and IP expansion, and candidate screening across IgG, ScFv, and VHH formats.
ConvergeAB™ is trained on over 1 trillion natural protein tokens, including 7 million antibody sequences, 3 million antibody–antigen pairs, and 100,000 fully characterized antibodies. This foundation enables affinity maturation, developability optimization, humanization, patent escape and IP expansion, and candidate screening across IgG, ScFv, and VHH formats.
Input
Sequence for optimization
Target product profile
Antigen sequence & structure
Input
You provide the biological elements: a mandatory protein sequence defining the final product, and optional regulatory regions. The promoter controls gene expression strength, while the 5’ and 3’ UTRs influence mRNA stability, translation efficiency, and overall protein yield, all of which can be optimized.
2nd
Antibody Generator
Predictors
Pairing
Solubility
Expressibility
Binding
Thermostability
Self association
Off targets
Aggregation
Immunogenicity
Predictors
Each predictor scores generated antibodies on key drug-development properties: correct heavy/light chain pairing, binding strength, off-target risk, cellular expression, solubility, thermostability, aggregation risk, and self-association, ensuring selected candidates are potent, stable, and manufacturable.
2nd
Docking
Output
48
Final candidates for experimental testing
Output
After generating and scoring many designs, the system selects a small set of high-confidence antibody candidates that bind strongly, demonstrate developability and manufacturability, and carry lower risk of failure—ready for experimental validation in the lab.
Input
Sequence for optimization
Target product profile
Antigen sequence & structure
Input
You provide the biological elements: a mandatory protein sequence defining the final product, and optional regulatory regions. The promoter controls gene expression strength, while the 5’ and 3’ UTRs influence mRNA stability, translation efficiency, and overall protein yield, all of which can be optimized.
2nd
Antibody Generator
Predictors
Pairing
Solubility
Expressibility
Binding
Thermostability
Self association
Off targets
Aggregation
Immunogenicity
Predictors
Each predictor scores generated antibodies on key drug-development properties: correct heavy/light chain pairing, binding strength, off-target risk, cellular expression, solubility, thermostability, aggregation risk, and self-association, ensuring selected candidates are potent, stable, and manufacturable.
2nd
Docking
Output
48
Final candidates for experimental testing
Output
After generating and scoring many designs, the system selects a small set of high-confidence antibody candidates that bind strongly, demonstrate developability and manufacturability, and carry lower risk of failure—ready for experimental validation in the lab.
The Data Behind Our System
The Data Behind Our System
Converge data sources
Acquisition of data from data partners
Extraction from global patent filings
Large scale curation of public data
Our proprietary experimental data
Training dataset
7M
antibody sequences
100K
fully characterized antibodies
3M
antibody antigen pairs
1T
natural protein tokens
Iteratively built through experiments (active learning)
Customer private instance
Customers can use their own proprietary data for fine tuning in an exclusive private environment.
Customer’s data is never used to train Converge’s base platform.
Converge data sources
Training dataset
Customer private instance
