license: cc-by-4.0
language:
- en
tags:
- protein-ligand
- structural-biology
- drug-discovery
- PXR
- cofolding
- PoseBusters
pretty_name: Structure184 Five-Method PXR Cofolding Dataset
size_categories:
- 10K<n<100K
configs:
- config_name: models
data_files:
- split: models
path: data/models.parquet
- config_name: posebusters_full
data_files:
- split: models
path: data/posebusters_full.parquet
- config_name: ligands
data_files:
- split: ligands
path: data/ligands.parquet
- config_name: truth_structures
data_files:
- split: structures
path: data/truth_structures.parquet
Structure184 five-method PXR cofolding dataset
This repository contains 92,000 PXR–ligand cofolded models:
- 184 ligands
- 20 seed positions
- 5 samples per seed
- Boltz2, Chai-1, ESMFold2, OpenFold3, and Protenix
- 18,400 models per method
Models use the governed prepared top solution-state ligand representation. Coordinates are harmonized to the 1NRL PXR frame using a 162-Cα core. The original generated coordinates are preserved up to that rigid alignment.
Repository contents
| Path | Contents |
|---|---|
data/models.parquet |
Primary 92,000-row table: identifiers, confidence, structural metrics, OpenStructure scores, chemistry features, ProLIF, and compact PoseBusters status. |
data/posebusters_full.parquet |
Full PoseBusters 0.6.5 report for 91,734 topology-safe models. |
data/ligands.parquet |
One row per ligand and prepared state. |
data/truth_structures.parquet |
Reference-structure metadata. |
structures/<method>.tar.zst |
Aligned model CIFs, one archive per method. |
structures/truth_structures.tar.zst |
Aligned reference structures. |
COLUMN_GUIDE.md |
Concise column descriptions. |
MANIFEST.json and checksums.sha256 |
Counts, sizes, and SHA-256 checksums. |
models.parquet has 119 columns. structure_archive and structure_member locate each CIF.
Load the tables
from datasets import load_dataset
models = load_dataset("dargason/structure184-five-method-cofolding", "models", split="models")
posebusters = load_dataset("dargason/structure184-five-method-cofolding", "posebusters_full", split="models")
Or with pandas:
import pandas as pd
models = pd.read_parquet("hf://datasets/dargason/structure184-five-method-cofolding/data/models.parquet")
Extract structures
tar --use-compress-program=unzstd -xf structures/boltz2.tar.zst
Archive members follow method/ligand_id/model_id.cif. Join them through structure_member.
PoseBusters
PoseBusters was recomputed from scratch using generated ligand coordinates, authoritative prepared-state topology, and generated receptor coordinates.
- Computed: 91,734
- Explicit topology quarantine: 266
- Passed all configured checks: 75,668
- Failed one or more configured checks: 16,066
Quarantined rows remain in models.parquet with posebusters_result_status=not_run_topology_quarantine.
Important interpretation notes
- PoseBusters is physical-validity QC, not pose-accuracy evidence.
protein_ligand_iptmis the primary model-native confidence field;iptm_source_keyrecords the engine-specific source.- Columns beginning
official_ost_, plus truth-relative RMSD and centroid-distance fields, are evaluation labels. Do not use them as blind model-selection inputs. - ESMFold2 models are the campaign's no-MSA, 50-step protocol, not upstream-default ESMFold2.
- The public tables remove machine-local paths. File hashes and source-group identifiers are retained.
- This release does not claim that every engine parameter can be reconstructed from the public tables alone.
Source and attribution
The ligand set and challenge context come from the OpenADMET PXR Induction Blind Challenge and its public challenge dataset. Please cite the OpenADMET challenge and this dataset repository when using these models or derived tables.
Repository size
The packaged repository is approximately 4.6 GB compressed. The Parquet tables and .tar.zst structure archives are configured for Git LFS through .gitattributes.
License
The tables, generated/aligned structures, truth-relative metrics, aligned truth structures, packaging, and documentation in this repository are provided under CC BY 4.0. Model-generating software remains under its respective license. See LICENSE.