| --- |
| 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 |
|
|
| ```python |
| 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: |
|
|
| ```python |
| import pandas as pd |
| |
| models = pd.read_parquet("hf://datasets/dargason/structure184-five-method-cofolding/data/models.parquet") |
| ``` |
|
|
| ## Extract structures |
|
|
| ```bash |
| 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_iptm` is the primary model-native confidence field; `iptm_source_key` records 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](https://openadmet.ghost.io/announcing-the-next-openadmet-blind-challenge-predicting-pxr-induction/) and its [public challenge dataset](https://huggingface.co/datasets/openadmet/pxr-challenge-train-test). 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`. |
|
|
