LLM-powered antibody design that accelerates discovery by 50% and achieves 95%+ project success rates. From de novo generation to affinity maturation — all in silico first.
AI antibody design is the application of machine learning — specifically large language models trained on vast antibody sequence and structural databases — to computationally design, screen, and optimize antibody candidates before any wet-lab experiment begins.
Traditional antibody discovery relies on biological processes: immunizing animals, generating hybridomas, or screening phage display libraries. These approaches are slow, expensive, and constrained by biological diversity. AI antibody design breaks these constraints. By learning the underlying grammar of antibody sequences — which residues drive affinity, stability, and manufacturability — AI models can explore sequence spaces orders of magnitude larger than any physical library.
AntibodyLLM's proprietary platform was built specifically for antibody engineering. Unlike generic protein language models, our AI is trained on curated, annotated antibody datasets with validated structure-function annotations. This domain specificity delivers substantially higher prediction accuracy for antibody-relevant properties including CDR loop conformation, paratope-epitope complementarity, and biophysical developability.
Generate entirely new antibody sequences optimized for your target antigen. No animal immunization required. Particularly powerful for membrane proteins, GPCRs, and highly conserved epitopes where traditional immunization fails.
AI-guided affinity maturation explores CDR mutations systematically to improve binding affinity by 10–100× while maintaining or improving selectivity and stability. Typical turnaround: 2–3 weeks vs. 3–6 months for traditional error-prone PCR approaches.
Convert rodent or camelid antibody sequences to human frameworks using AI-guided CDR grafting and back-mutation optimization. Our models predict immunogenicity risk for each variant, delivering humanized sequences with retained function and minimal immunogenic potential.
AI screening of candidates for predicted aggregation propensity, thermal stability (Tm), isoelectric point, hydrophobicity, and expression yield. Eliminate developability liabilities before investing in cell line development or scale-up.
You provide antigen sequence, structure (if available), epitope preferences, and desired antibody format. Our team assesses AI design feasibility and proposes a design strategy within 48 hours.
Our LLM generates thousands of candidate sequences. Multi-objective AI scoring filters for predicted affinity, selectivity, stability, and manufacturability simultaneously, down-selecting to the top 20–50 candidates.
Top candidates are gene-synthesized and transiently expressed in our proprietary CHO system. Miniaturized expression screening identifies high-yielding candidates within 1–2 weeks.
Purified candidates are characterized by ELISA, SPR/BLI kinetics, cross-reactivity panel, and thermal stability. Binding data feeds back into the AI model, improving future design iterations.
Validated lead antibodies are delivered with full characterization reports. Optional next steps include stable cell line development for scale-up or recombinant protein expression for research use.
Oncology (checkpoint inhibitors, ADC targets), autoimmune (cytokine blockade, B-cell depletion), infectious disease (neutralizing antibodies). AI enables optimization for neutralization potency, effector function, and half-life.
High-specificity antibody pairs for sandwich ELISA, lateral flow assays, and chemiluminescence immunoassays. AI optimizes for capture/detection pair orthogonality and cross-reactivity rejection.
Application-validated antibodies for flow cytometry, IHC, IP-MS, and Western blot. Recombinant production ensures lot-to-lot consistency that polyclonal reagents cannot provide.
AI antibody design uses large language models trained on millions of antibody sequences to generate and optimize antibody candidates computationally. The AI learns sequence-structure-function relationships, enabling design of novel antibodies with desired binding, stability, and manufacturability before any wet-lab experiment begins.
Traditional hybridoma development takes 6–18 months with high attrition. AntibodyLLM's AI platform reduces time to validated leads by 50%, achieves 95%+ project success rates, and enables in silico pre-screening of thousands of candidates — dramatically reducing cost and experimental waste.
AI design is especially powerful for difficult targets: membrane proteins and GPCRs, highly conserved antigens requiring cross-species selectivity, conformation-specific epitopes, and post-translationally modified targets. It also excels at bispecific antibody design where chain pairing must be engineered computationally.
Standard projects take 4–8 weeks from target submission to delivery of validated lead candidates. This includes computational design (1–2 weeks), gene synthesis and expression (1–2 weeks), binding validation (1–2 weeks), and lead characterization. Timeline varies based on target complexity and validation requirements.
Deliverables include: verified VH/VL sequence files, expressed and purified antibody protein (mg quantities), binding affinity data (ELISA/SPR/BLI), specificity profiling report, developability assessment (thermal stability, aggregation, charge variants), and downstream recommendations for scale-up.
Yes. Our AI humanization service converts rodent or camelid antibody sequences to human frameworks using AI-guided CDR grafting and back-mutation optimization. AI models predict immunogenicity risk for each variant, delivering humanized sequences with retained binding function and minimized immunogenic potential for clinical development.
Tell us about your target and we'll propose an AI design strategy within 48 hours.
Start Your AI Design Project