Datasets:

DeepBioScientific
/

MIRAGE

license: other
license_name: mixed-see-below
license_link: https://github.com/DeepBio-Scientific/MIRAGE#provenance--licensing
task_categories:
  - tabular-regression
tags:
  - binding-affinity
  - drug-discovery
  - protein-ligand
  - benchmark
  - data-leakage
pretty_name: MIRAGE
configs:
  - config_name: redundancy
    data_files: redundancy/test-*.parquet
    default: true
  - config_name: temporal
    data_files: temporal/test-*.parquet

MIRAGE: Measuring Interpolation and Redundancy in Affinity GEneralization

A benchmark for measuring whether a protein–ligand binding-affinity model generalises beyond protein families that are heavily represented in the PDB, or whether its reported accuracy is carried by that redundancy.

Frontier co-folding affinity models (Boltz-2, Nesso-1, …) report Pearson ≈ 0.6 on standard benchmarks. When the same models are stratified by protein-family size, that accuracy is concentrated on well-represented families and collapses to ≈ 0 on singleton families — exactly the case that matters when starting a program against a novel target.

Configs

`redundancy` (18,759 rows, default)

PDBbind-derived protein–ligand complexes with experimental affinity, annotated with protein-family size so accuracy can be stratified by redundancy.

column	description
`id`	PDB code
`sequence`	protein chain (single-letter)
`smiles`	ligand SMILES (from the deposited structure)
`pK`	experimental −log10(Kd/Ki/IC50); higher = stronger
`affinity_type`	`Kd` / `Ki` / `IC50`
`year`	PDB deposition year
`family_id`	MMseqs2 30%-identity cluster representative
`family_size`	number of complexes in that family (the redundancy axis)
`redundancy_bin`	`1`, `2-5`, `6-20`, `21-80`, `81-300`, `301+`
`ligand_nn_tanimoto`	ECFP4 nearest-neighbour similarity to any other ligand
`in_core`	member of the balanced 3,360-target quick-eval subset

`temporal` (649 rows)

One structurally novel, post-2021-09-30 target (OpenBind EV-A71 2A protease), with measured KD and shipped baselines. Tests within-target ranking on data that no model with a ≤ 2021 cutoff could have seen.

column	description
`id`	Fragalysis code
`sequence`	target protein
`smiles`	ligand SMILES
`pKD`	experimental −log10(KD); higher = stronger
`baseline_molecular_weight`, `baseline_clogp`, `ref_boltz2`	reference predictions

Usage

import mirage

def my_model(sequence, smiles):
    return score               # higher = stronger binder

print(mirage.run_redundancy(my_model).summary())
print(mirage.run_temporal(my_model).summary())

See https://github.com/DeepBio-Scientific/MIRAGE for the harness, baselines, and CLI.

Provenance & licensing

Redundancy set is derived from PDBbind (protein sequences from the PDB; SMILES from deposited ligands; affinities from PDBbind's curated literature values). Only derived annotations are redistributed here, under MIT. Users should observe PDBbind's own terms when using the underlying structures.
Temporal set is derived from the OpenBind A71EV2A release (CC0) and its public benchmark repository.

Family sizes are computed with MMseqs2 at 30% sequence identity / 80% coverage.