Creative Biolabs

AI-Driven Antibody Solutions

Accelerate Discovery and Engineering

Creative Biolabs equips biopharma and biotech teams with AI-driven antibody solutions—compressing R&D cycles by uniting predictive modeling, multi-omics data mining, and high-throughput validation. From de novo sequence generation to engineering and developability assessment, we translate complex datasets into decision-ready leads and designs that are built for scale.

Fig. 1. Antibody 3d structure (Creative Biolabs Authorized)

Why Choose Our AI-Driven Antibody Solutions

Traditional antibody programs are slow and uncertain—multi-year timelines, low hit rates from large libraries, and expensive, late-stage failures caused by poor developability or off-target binding. Wet-lab iteration alone rarely keeps pace with today's target complexity and modality diversity.

Creative Biolabs integrates AI with rigorous experimental workflows to break these bottlenecks. Foundation models and structure predictors narrow design space; ML-based filters surface high-value variants; tight in silico-in vitro loops accelerate convergence on specific, stable, and scalable antibody candidates.

Traditional Approach

  • ✗ Multi-year timelines and high variability
  • ✗ Relies on physical library screening and manual iteration
  • ✗ Late-stage failure due to poor developability

AI-Driven Approach

  • ✓ Compressed R&D cycles and higher success rates
  • ✓ Generative design and multi-objective optimization
  • ✓ Early prediction and mitigation of developability risks

AI Antibody Services: Discovery, Screening, Design & Engineering

AI-Driven Antibody Discovery

Generative AI models create de novo amino-acid sequences, expanding the search space and generating up to 10× more unique clusters than conventional workflows. Creative Biolabs rapidly advances the most promising designs to bench verification—confirming expression, folding, and binding—while uncovering novel sequence motifs linked to new binding modalities and mechanisms.

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Fig. 2. AI-driven antibody discovery (Creative Biolabs Authorized)
Fig. 3. AI-driven antibody screening (Creative Biolabs Original)

AI-Driven Antibody Screening

Our in silico screening platform enhances precision before assays begin. High-throughput virtual screening identifies high-affinity candidates, while physics-based simulations estimate antigen-binding strength. Only top-scoring antibodies advance to ELISA, flow, or display validation, reducing bench workload and increasing hit success.

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AI-Driven Antibody Design

Structure predictors reveal 3D architectures directly from sequences and generate PDB models for docking, interaction analysis, and stability assessment. Dynamic visualization highlights epitopes and key residues for CDR or framework edits, guiding rational modification to enhance affinity, specificity, and stability before synthesis.

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Fig. 4. AI-driven antibody design (Creative Biolabs Authorized)
Fig. 5. AI-driven antibody engineering (Creative Biolabs Authorized)

AI-Driven Antibody Engineering

Developability risks are addressed from the start. Sequence- and structure-aware AI models flag aggregation and solubility issues early, recommending substitutions to improve stability, expression yield, and manufacturability. This predictive foresight minimizes re-engineering and ensures seamless progression from design to production.

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AI-Enabled Antibody Workflow:
From Data Onboarding to Candidate Nomination

1. Project Scoping & Data Integration

Define targets and import all relevant structural, sequence, or omics datasets.

Leverage AI to analyze disease pathway data and epigenomic information, precisely identifying targets with high developability potential. AI also predicts target tissue distribution and off-target risks.

2. AI-Assisted Discovery

Combine de novo sequence generation with repertoire mining to expand diversity.

Based on generative deep learning models, the platform can design or optimize V/J/D gene segment combinations from scratch, generating billions of novel antibody sequences with ideal CDR loop structures and high-affinity potential.

3. Virtual Screening

Rank sequences by predicted binding, liability, and physicochemical traits.

Using fast docking and molecular dynamics simulations, AI conducts high-throughput virtual screening on the generated sequence library, predicting binding affinity (Kd) and stability (Tm) with the target.

4. Structure Prediction & Docking

Model Fv regions, assess CDR conformations, and evaluate complex stability.

This step ensures structural integrity and precise binding evaluation, moving beyond simple sequence-based prediction to rational design guidance.

5. Multi-Objective Optimization

Edit for affinity, specificity, solubility, and manufacturability.

AI identifies potential flaws (e.g., deamidation sites, aggregation propensity) and suggests optimal mutation points, guiding humanization, stabilization, and affinity maturation.

6. Wet-Lab Validation

Express and test high-value candidates; feed data back for retraining.

Experimental results (e.g., affinity, function) serve as real-world data input for the next round of AI optimization, creating a highly efficient closed-loop iterative process.

7. Candidate Nomination

Deliver ranked variants, annotated structures, and developability reports.

After all optimization and assessment steps, the antibody with the best overall performance is selected as the Preclinical Candidate (PCC). The platform provides comprehensive data reports and technical support for IND submission.

AI Antibody Technology Stack & Platform Capabilities

Structure Prediction and Modeling

Deep-learning tools (IgFold, ABodyBuilder2, DeepAb) predict Fv and CDR-H3 conformations with atomic-level accuracy, accelerating structure-guided antibody discovery and optimization.

Generative AI and Language Models

Transformer-based models like ProteinMPNN and AntiBERTa generate de novo antibody sequences, enhancing novelty, structural diversity, and developability across species and targets.

Virtual Screening and Affinity Prediction

AI-powered docking combines physics-based scoring and ML filters to predict antibody-antigen affinity, rapidly identifying high-binding candidates before wet-lab assays.

Developability and Manufacturability Analytics

CamSol- and TAP-inspired predictors assess solubility, aggregation, charge, and stability, ensuring candidates meet developability standards for scalable biomanufacturing.

Multi-Omics Data Integration

Integrates genomic, proteomic, and repertoire datasets to reveal sequence-function relationships, continuously refining antibody models for accuracy and predictive performance.

End-to-End Informatics Workflow

Automated feedback between modeling and experimental validation enables real-time learning, shortening design cycles and improving decision-making throughout antibody R&D.

AI-Driven One-Stop Antibody Discovery Platforms

Advantages of Creative Biolabs' AI Antibody Solutions

End-to-end integration

Discovery to engineering—within one secure platform.

Faster lead generation

Accelerated timelines and reduced experimental burden.

Early Risk Detection

Flagging aggregation, solubility, and manufacturability issues.

Structural Guidance

Informing design for novel or challenging epitopes.

Flexible Formats

Supports IgG, Fab, scFv, bispecifics, and custom scaffolds.

Transparent Deliverables

Ranked sequences, model rationales, and validation recommendations.

Applications of AI in Antibody Discovery, Optimization & Developability

Rapid Lead Generation for Novel Targets

AI expands the antibody search landscape, producing diverse human-compatible panels even when antigen data are limited.

Humanization & Liability Reduction

Machine-learning models recommend framework edits that preserve paratope geometry while minimizing immunogenic risk and charge asymmetry. This is a critical step in ensuring the safety and effectiveness of long-term use in humans, where AI plays an irreplaceable role in global optimization.

Affinity & Specificity Optimization

Structure-aware redesign improves binding energy and reduces off-target interactions, confirmed by empirical assays. Through simulating the impact of mutations on binding free energy, AI can quickly iterate and converge on the most effective affinity-enhancing solution.

Bispecific and Multispecific Engineering

Predicts orientation, linker length, and interface geometry, enabling rational design before cloning. The complex assembly and stability challenges faced by multispecific antibodies are significantly simplified, shortening the time from concept to a producible molecule.

Developability-First Engineering

Applies CamSol and TAP-style heuristics for solubility, expression yield, and stability in upstream selection. Ensures the final candidate antibody is not only effective but also easy to produce, store, and use clinically, maximizing its commercial value.

Knowledge Mining from Antibody Big Data

Trained on millions of sequences from OAS and SAbDab, AI uncovers cross-species structural motifs to inform future campaigns. This continuous learning and evolution capability of the platform brings sustained innovation to your projects.

Trusted by Global R&D Teams

Creative Biolabs is recognized by biopharma innovators worldwide for delivering measurable acceleration, reproducibility, and clarity from AI modeling to experimental validation.

Fig. 6. Abbvie logo (Creative Biolabs Authorized)
Fig. 7. AstraZeneca logo-(Creative Biolabs Authorized)
Fig. 8. Pfizer logo (Creative Biolabs Authorized)
Fig. 9. Novartis logo (Creative Biolabs Authorized)
Fig. 10. GSK logo (Creative Biolabs Authorized)
Fig. 11. Boehringer Ingelheim logo (Creative Biolabs Authorized)

Customer Reviews

“The AI-based pre-screening pipeline from Creative Biolabs significantly reduced our experimental workload and turnaround time. We achieved higher-quality antibody hits with stronger binding profiles, saving both time and budget across our early discovery program.”

Director of Antibody Engineering
U.S.

“Their predictive developability analytics helped us identify solubility and aggregation risks before scale-up. The early insights and recommended sequence edits prevented costly delays and ensured smooth manufacturing transfer.”

Head of Protein Sciences
Europe

“For a completely novel antigen with limited prior data, Creative Biolabs' generative AI platform delivered diverse, human-compatible antibody panels in one design cycle—something our traditional workflow had never achieved.”

Discovery Lead
Asia-Pacific

“We collaborated with Creative Biolabs to refine several low-expression antibodies. Their AI-assisted engineering pinpointed key framework liabilities, and the optimized variants showed a 4× improvement in yield and biophysical stability.”

Principal Scientist
Global Biotech Company

FAQs

AI models learn statistical patterns from large antibody datasets, then generate new sequences under germline and liability constraints. This expands sequence diversity several-fold while maintaining realistic frameworks and CDR patterns, improving hit probability without simply enlarging physical libraries.
We apply state-of-the-art deep learning structure predictors specifically trained on antibody-like folds. These models reconstruct Fv and CDR conformations with high accuracy, enabling loop-level inspection, epitope alignment, and structure-guided edits before any experimental expression or characterization.
When no high-resolution structure is available, we combine sequence-based embeddings, homology information, and predicted 3D models. AI still guides antibody design by learning compatible paratope patterns, helping assemble informative panels under low-structure-information or early-discovery conditions.
We use sequence- and structure-based developability models built on published biophysical principles to assess charge distribution, hydrophobic patches, and self-interaction motifs. Candidates with high-risk signatures are flagged early, guiding sequence refinement before scale-up or formulation studies.
Yes. Our modeling framework evaluates chain pairing, spatial orientation, and linker geometry for bispecific formats. This in silico assessment helps pre-screen designs, reduce experimental trial-and-error, and maintain target engagement and stability requirements from the earliest engineering stages.
Typical projects deliver ranked antibody sequences, 3D structural models, developability and manufacturability assessments, and written design rationales. We also provide practical recommendations for expression, purification, and follow-up assays, so each candidate is immediately usable by internal R&D teams.

References

  1. Leem, Jinwoo, et al. "Deciphering the language of antibodies using self-supervised learning." Patterns 3.7 (2022). https://doi.org/10.1101/2021.11.10.468064
  2. Ruffolo, Jeffrey A., et al. "Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies." Nature communications 14.1 (2023): 2389. https://doi.org/10.1038/s41467-023-38063-x
  3. Abanades, Brennan, et al. "ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins." Communications Biology 6.1 (2023): 575. https://doi.org/10.1038/s42003-023-04927-7
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