Datasetpublic

mansoorbaloch/chimera-bench

CHIMERA-Bench v1.0 A unified benchmark for epitope-specific antibody CDR sequence-structure co-design. Paper: CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design (ICLR 2026 GEM Workshop) Code: github.com/mansoorbaloch/chimera-bench Dataset Summary Property Value Complexes 2,922 PDB structures 2,721 Pre-computed features 2,941 .pt files Splits 3 (epitope-group, antigen-fold, temporal) Numbering schemes IMGT, Chothia Contact… See the full description on the dataset page: https://huggingface.co/datasets/mansoorbaloch/chimera-bench.

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Dataset Card

CHIMERA-Bench v1.0

A unified benchmark for epitope-specific antibody CDR sequence-structure co-design.

Paper: CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design (ICLR 2026 GEM Workshop)

Code: github.com/mansoorbaloch/chimera-bench

Dataset Summary

Property	Value
Complexes	2,922
PDB structures	2,721
Pre-computed features	2,941 `.pt` files
Splits	3 (epitope-group, antigen-fold, temporal)
Numbering schemes	IMGT, Chothia
Contact cutoff	4.5 A
Resolution cutoff	4.0 A
Baselines evaluated	11 methods, 6 paradigms

Download

This dataset contains binary PyTorch files (.pt) and PDB structures that require manual download. Use the HuggingFace CLI:

bash

export CHIMERA_DATA_ROOT=/path/to/chimera-bench-v1.0
huggingface-cli download mansoorbaloch/chimera-bench --repo-type dataset --local-dir $CHIMERA_DATA_ROOT

Directory Structure

chimera-bench-v1.0/
  README.md
  metadata/
    final_summary.csv           # 2,922 complexes with 32 columns
    excluded_complexes.csv      # 59 excluded complexes with reasons
    antibody_sequences.fasta    # VH+VL sequences for all complexes
    contamination_audit.json    # PLM training data overlap analysis
  splits/
    epitope_group.json          # Primary split (2338/292/292)
    antigen_fold.json           # Fold-based generalization (2338/292/292)
    temporal.json               # Prospective evaluation (2337/292/293)
  complex_features/             # Per-complex PyTorch tensors (2,941 files)
    {complex_id}.pt
  structures/                   # PDB structure files (2,721 files)
    {pdb}.pdb

Complex Features Format

Each .pt file is a Python dict with:

Sequences

—complex_id: str -- unique identifier ({pdb}{Hchain}{Lchain}_{Agchain})
—heavy_sequence, light_sequence, antigen_sequence: str -- one-letter AA

Coordinates

—heavy_atom14_coords: float32 (N_h, 14, 3) -- 14-atom representation
—heavy_atom14_mask: bool (N_h, 14) -- valid atom flags
—heavy_ca_coords: float32 (N_h, 3) -- CA-only coordinates
—Same for light_* and antigen_*

Annotations

—epitope_residues: list of (chain, resid, resname) tuples
—paratope_residues: list of (chain, resid, resname) tuples
—contact_pairs: list of (abchain, abresid, abresname, agchain, agresid, agresname, distance)

Numbering

—numbering: dict with imgt and chothia sub-dicts, each containing heavy and light lists of (resnum, icode, aa) tuples
—cdr_masks: dict with imgt and chothia sub-dicts, each containing heavy and light int lists (-1=framework; heavy: 0=H1, 1=H2, 2=H3; light: 3=L1, 4=L2, 5=L3)

Surface Features

—ag_surface_points: float32 (128, 3) -- sampled antigen surface points
—ag_surface_normals: float32 (128, 3)
—ag_surface_curvatures: float32 (128, 2) -- mean and Gaussian curvature
—ag_surface_chemical_feats: float32 (128, 6) -- hydropathy, charge, H-bond donor/acceptor, aromaticity, polarity
—Same for heavy_surface_* and light_surface_*

Splits

Split	Train	Val	Test	Generalization Axis
epitope_group	2,338	292	292	Unseen epitope patterns
antigen_fold	2,338	292	292	Unseen antigen folds
temporal	2,337	292	293	Prospective (by deposition date)

Each split JSON has keys train, val, test mapping to lists of complex_id strings.

Evaluation Metrics

Group	Metrics
Sequence quality	AAR, CAAR, PPL
Structural accuracy	RMSD (Kabsch-aligned CA), TM-score
Binding interface	Fnat, iRMSD, DockQ
Epitope specificity	EpiF1 (precision, recall, F1)
Designability	n_liabilities (NG, DG, DS, DD, NS, NT, M motifs)

Quick Start

python

import torch, json, pandas as pd

# Load metadata
summary = pd.read_csv("metadata/final_summary.csv")

# Load a split
with open("splits/epitope_group.json") as f:
    split = json.load(f)
print(f"Train: {len(split['train'])}, Val: {len(split['val'])}, Test: {len(split['test'])}")

# Load a complex
feat = torch.load(f"complex_features/{split['test'][0]}.pt", weights_only=False)
print(feat['complex_id'], feat['heavy_sequence'][:20], "...")
print(f"Epitope residues: {len(feat['epitope_residues'])}")
print(f"CDR-H3 (IMGT): positions where cdr_masks['imgt']['heavy'] == 2")

Citation

bibtex

@inproceedings{
ahmed2026chimerabench,
title={{CHIMERA}-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design},
author={Mansoor Ahmed and Nadeem Taj and Imdad Ullah Khan and Hemanth Venkateswara and Murray Patterson},
booktitle={ICLR 2026 Workshop on Generative and Experimental Perspectives for Biomolecular Design},
year={2026},
url={https://openreview.net/forum?id=PyZvVIJbSy}
}

License

Data: CC-BY 4.0. Code: MIT.