File size: 4,372 Bytes
0c42f71 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | # CHIMERA-Bench v1.0
A unified benchmark for epitope-specific antibody CDR sequence-structure co-design.
**Paper**: CHIMERA-Bench: A Gold-Standard Benchmark for Epitope-Specific Antibody Design (ICLR GEM Workshop 2026)
## 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, 8 paradigms |
## 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 (ab_chain, ab_resid, ab_resname, ag_chain, ag_resid, ag_resname, 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
- **epitope_group**: clusters by epitope residue fingerprint; test set has epitope patterns unseen during training
- **antigen_fold**: clusters by antigen identity; test set has entirely unseen antigens
- **temporal**: splits by PDB deposition date; simulates prospective deployment
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{chimera2026,
title={CHIMERA-Bench: A Gold-Standard Benchmark for Epitope-Specific Antibody Design},
booktitle={ICLR 2026 Workshop on Generative and Experimental Perspectives for Biomolecular Design (GEM)},
year={2026}
}
```
## License
Data: CC-BY 4.0. Code: MIT.
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