AI Antibody Design: From Target to Candidate in Minutes

The Antibody Design Challenge

Antibodies are the workhorses of modern therapeutics. Over 100 antibody drugs are approved worldwide, treating cancer, autoimmune diseases, and infections. But developing a therapeutic antibody traditionally requires months of animal immunization, hybridoma screening, and iterative affinity maturation — a slow and expensive process.

Computational antibody design aims to compress this timeline from months to days. By combining AI structure prediction with inverse folding, researchers can now generate antibody candidates in silico, predict their structures, optimize their binding regions, and assess developability — all before touching a pipette.

The AI Antibody Design Pipeline

A modern computational antibody design workflow involves three key stages, each powered by a specialized AI model:

• Structure prediction (ImmuneBuilder): Predict the 3D structure of your starting antibody from its heavy and light chain sequences
• CDR design (AntiFold): Generate optimized CDR loop sequences that maintain the structural scaffold while improving binding properties
• Validation (ESMFold): Verify that designed sequences fold into the expected structure by running structure prediction on the new variants

Step 1: Predict Antibody Structure with ImmuneBuilder

ImmuneBuilder is a deep learning model specifically trained to predict antibody 3D structures. Unlike general-purpose protein structure predictors, it understands the unique architecture of immunoglobulins — the conserved framework regions and the highly variable CDR loops.

Given heavy and light chain sequences, ImmuneBuilder predicts the paired antibody structure including accurate CDR loop conformations. This is the foundation for downstream CDR design, because AntiFold needs a 3D structure as input.

import os, requests, time

API_KEY = os.environ["SCIROUTER_API_KEY"]
BASE = "https://api.scirouter.ai/v1"
HEADERS = {"Authorization": f"Bearer {API_KEY}"}

# Predict antibody structure
job = requests.post(f"{BASE}/antibodies/structure", headers=HEADERS, json={
    "model": "immunebuilder",
    "heavy_chain": "EVQLVESGGGLVQPGGSLRLSCAASGFTFSDYWMHWVRQAPGKGLVWVSRIN"
                   "SDGSSTSYADSVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCAR",
    "light_chain": "DIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKLLIYAAS"
                   "SLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSYSTPLT",
}).json()

# Poll for results
while True:
    result = requests.get(
        f"{BASE}/antibodies/structure/{job['job_id']}", headers=HEADERS
    ).json()
    if result["status"] == "completed":
        break
    if result["status"] == "failed":
        raise RuntimeError(result.get("error"))
    time.sleep(3)

print(f"Structure confidence: {result['confidence']:.2f}")
print(f"CDR-H3 pLDDT: {result['cdr_scores']['H3']:.1f}")

with open("antibody.pdb", "w") as f:
    f.write(result["pdb"])
print("Antibody structure saved")

Step 2: Design CDR Sequences with AntiFold

With the antibody structure in hand, AntiFold can design new CDR sequences optimized for the target binding geometry. AntiFold is an inverse folding model fine-tuned on antibody structures — it understands which sequence changes are compatible with the CDR loop conformations required for binding.

You can design all six CDRs simultaneously or focus on specific loops. CDR-H3, the most variable and often most critical loop, is the most common target for design.

# Design new CDR sequences
design_resp = requests.post(f"{BASE}/antibodies/design", headers=HEADERS, json={
    "model": "antifold",
    "pdb": result["pdb"],        # Structure from ImmuneBuilder
    "target_cdrs": ["H3"],       # Focus on CDR-H3
    "num_designs": 16,           # Generate 16 variants
    "temperature": 0.2,          # Moderate diversity
}).json()

print(f"Generated {len(design_resp['designs'])} CDR-H3 variants:\n")
for i, design in enumerate(design_resp["designs"]):
    print(f"  Variant {i+1}: {design['cdr_h3_sequence']}")
    print(f"    Score: {design['score']:.3f}")
    print(f"    Mutations vs parent: {design['num_mutations']}")

Step 3: Validate Designs

After generating CDR variants, validate that the full antibody with the new CDR sequence still folds correctly. Run each variant through structure prediction and check that the overall fold is maintained and the CDR loop adopts the desired conformation.

# Validate top 3 designs
for design in design_resp["designs"][:3]:
    full_sequence = design["full_heavy_chain"]  # Includes new CDR-H3

    fold_job = requests.post(f"{BASE}/proteins/fold", headers=HEADERS, json={
        "sequence": full_sequence,
        "model": "esmfold",
    }).json()

    while True:
        fold_result = requests.get(
            f"{BASE}/proteins/fold/{fold_job['job_id']}", headers=HEADERS
        ).json()
        if fold_result["status"] in ("completed", "failed"):
            break
        time.sleep(3)

    if fold_result["status"] == "completed":
        print(f"CDR-H3: {design['cdr_h3_sequence']}")
        print(f"  Mean pLDDT: {fold_result['mean_plddt']:.1f}")
        print()

The Antibody Design Lab on SciRouter

SciRouter's Antibody Design Lab wraps the entire pipeline described above into a single workflow accessible from the dashboard. You paste in your antibody sequences, select which CDRs to optimize, and the platform runs structure prediction, CDR design, and validation automatically.

The lab provides a visual comparison of designed variants, highlighting mutations relative to the parent sequence and flagging any designs with structural concerns. This makes it accessible to researchers who want AI-powered antibody design without writing code.

When to Use Computational Antibody Design

• Affinity maturation: Generate CDR variants to improve binding affinity to a known target
• Humanization: Redesign non-human antibody frameworks to reduce immunogenicity while preserving binding
• Cross-reactivity engineering: Design variants that bind related targets or avoid off-target interactions
• Library design: Create focused libraries for phage display or yeast display screening campaigns

Next Steps

Antibody design is one component of a broader drug discovery pipeline. After generating candidates, you may want to predict their binding to the target using Chai-1 complex prediction or assess developability properties with molecular property calculations.

For sequence design on non-antibody proteins, see our ProteinMPNN tutorial. Or sign up for a free SciRouter API key and try the Antibody Design Lab today with 500 free credits per month.