task_categories:
- tabular-regression
- feature-extraction
tags:
- antibody
- antibody-developability
- assay-metadata
- multimodal-learning
AbAssayBench
This dataset repository contains the processed data package for AbAssayBench, a multi-endpoint antibody developability benchmark. The release combines the FLAb2.0-derived antibody measurements used for model development with the PROPHET-Ab measurements used for external validation.
The repository is intended to be used together with the MAP-Ab source code:
https://github.com/gu-yaowen/MAP-Ab.
Package layout
| Path | Contents |
|---|---|
tables/ |
Release tables with stable IDs and repository-relative file references. |
assay_metadata/ |
One JSON file per referenced assay metadata record. |
structures/ |
Antibody PDB files used by the processed structure features. |
features/ |
Precomputed sequence, structure, and assay-metadata feature stores. |
splits/ |
Long-format split assignments and split-size summaries. |
results/ |
Model predictions, endpoint metrics, tables, and publication figures. |
schemas/ |
Machine-readable description of the release contract. |
manifests/ |
Dataset, assay, structure, metadata, and file manifests. |
Main tables
tables/flab2_measurements.csv and tables/prophet_ab_measurements.csv retain
the measurement-level labels and canonical assay columns. Both tables add the
following release identifiers:
measurement_id: stable identifier for one measurement row;antibody_id: stable identifier for a heavy/light-chain pair;endpoint_id: stable identifier for a property and endpoint definition;structure_id: stable identifier for the source structure reference;assay_metadata_file: path relative to this repository;structure_file: path relative to this repository;property: normalized broad property label;source_row_index: original row index in the source table.
The numeric measurement label is stored in the original fitness column.
value_definition identifies the endpoint definition, while assay_id
identifies the assay unit. The broad property labels are aggregation,
expression, immunogenicity, pharmacokinetics, polyreactivity, and
thermostability for the FLAb2.0-derived data; PROPHET-Ab retains its five
benchmark property labels in the same schema.
Feature-store contract
Each feature directory contains features.npy, metadata.csv, and, when
available, feature_info.json. Row i in features.npy corresponds to row
i in metadata.csv; measurement_id is the preferred join key. Feature
arrays are stored as float32 and are not re-normalized by this release.
Directory names identify the feature family, including esmc_600m, ism_3b,
propermab_struct, and the assay-metadata embedding stores.
Splits and results
The split package is intentionally long-format: filter
splits/split_assignments.csv by split_family and replicate, then join on
measurement_id. The provided results are frozen outputs from the project
analysis and are not required to reproduce the feature stores.
Downloading from Hugging Face
hf download yg3191/AbAssayBench \
--repo-type dataset \
--local-dir ./AbAssayBench
Loading example
from pathlib import Path
import numpy as np
import pandas as pd
root = Path("AbAssayBench")
measurements = pd.read_parquet(root / "tables/flab2_measurements.parquet")
feature_meta = pd.read_csv(root / "features/flab2/esmc_600m/metadata.csv")
features = np.load(root / "features/flab2/esmc_600m/features.npy", mmap_mode="r")
assert len(feature_meta) == features.shape[0]
The checksum file at manifests/checksums.sha256 covers the release files.
The source-attribution manifest records the component datasets and their roles;
source redistribution conditions should be checked before public reuse.