ProteinsCPU1 credits

AbLang2 — Antibody Chain Embeddings

Generate 768-dim antibody chain embeddings for ML workflows

Convert any heavy or light chain sequence into a dense 768-dimensional vector using AbLang2, the antibody-specific language model from Oxford OPIG. Use these embeddings for CDR-aware similarity search, naturalness scoring, and downstream antibody engineering ML tasks.

$0.01

per API call

credits per call

/v1/antibody-lab/embed

API endpoint

Features

768-dimensional antibody chain embeddings

Works on heavy or light chain VH/VL sequences

CPU-only — no GPU needed

Captures CDR-aware context

Deterministic hash fallback for reliability

Complementary to /v1/antibody-lab/humanness scoring

Quick Start

AbLang2 — Python Examplepython

import requests

API_KEY = "sk-sci-your-key-here"
url = "https://scirouter.ai/v1/antibody-lab/embed"

# Trastuzumab VH
vh = "EVQLVESGGGLVQPGGSLRLSCAASGFNIKDTYIHWVRQAPGKGLEWVARIYPTNGYTRYADSVKG" \
     "RFTISADTSKNTAYLQMNSLRAEDTAVYYCSRWGGDGFYAMDYWGQGTLVTVSS"

response = requests.post(
    url,
    json={"sequence": vh},
    headers={"Authorization": f"Bearer {API_KEY}"}
)
data = response.json()["data"]
print(f"Dimension: {data['dimension']}")  # 768
print(f"Model: {data['model']}")          # antibody_ablang2