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xX-its-amit-Xx/pxr-structure-pose-pool

PXR Structure Challenge — Full Multi-Model Pose Pool (184 ligands) Every protein–ligand pose generated during the OpenADMET PXR (pregnane X receptor / NR1I2) structure-prediction challenge, released openly with per-pose labels so the community can reuse the compute already spent — and, we hope, crack the problem this data makes visible. What's here poses/<model>/<SID>.pdb — one best pose per (model, ligand). Protein chain A + ligand (resname LIG). 15 models, up… See the full description on the dataset page: https://huggingface.co/datasets/xX-its-amit-Xx/pxr-structure-pose-pool.

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Dataset Card

PXR Structure Challenge — Full Multi-Model Pose Pool (184 ligands)

Every protein–ligand pose generated during the OpenADMET PXR (pregnane X receptor / NR1I2) structure-prediction challenge, released openly with per-pose labels so the community can reuse the compute already spent — and, we hope, crack the problem this data makes visible.

What's here

—`poses/<model>/<SID>.pdb` — one best pose per (model, ligand). Protein chain A + ligand (resname LIG). 15 models, up to 184 ligands each (2,246 poses total). Filenames use an uppercase leading `X` (e.g. X00011-1.pdb); the canonical ligand id in manifest.csv is lowercase (x00011-1). Map with sid = filename.lower(). (The Hub rejects paths beginning x00…, which it reads as a null-byte escape — hence the uppercase.)
—`manifest.csv` — the labels, one row per pose:

column	meaning
`sid`	ligand id (`xNNNNN-1`)
`model`	predictor (boltz1, boltz1x, of3ft, protenixv2, decaf, esm2full/shallow/none, esmfold2fast, rf3, apo, af3iface, af3massive, chai, restr)
`smiles`	ligand SMILES
`population`	`fragment` (73) or `drug_like` analog (111)
`series`	congeneric series id
`mw, clogp, tpsa, rotb, heavy`	ligand descriptors
`pEC50`	measured PXR activation where available
`lig_plddt`	model confidence (ligand pLDDT surrogate) where reported
`rmsd_to_medoid`	this pose's distance from the cross-model consensus (A)
`disagreement_mean_pw_rmsd`	ligand-level cross-model disagreement (A)
`consensus_frac`, `medoid_model`, `n_models`	consensus metadata

The open problem this data exists for

Post-hoc analysis showed the pool contains sub-2 A poses for most ligands, but no confidence signal — and not even a GT-trained ML model on physics features — can reliably select them (the "selection wall"). The full autopsy, including an interactive 3D viewer, is at the project's GitHub Pages report. If your method can learn to pick the good pose already sitting in this pool, that is exactly the gap this release targets.

Ground truth

No crystal structures were released for the 184 blind test ligands. RMSD-style labels here are cross-model consensus geometry, not crystal RMSD. For true-RMSD work, use the homologous PXR holo crystal panel (also in the project repo).

Acknowledgment — LatchBio 💚

The final leg of this campaign, including additional AlphaFold3 pose sampling, was sponsored by [LatchBio](https://latch.bio) ($500 compute credits). Those poses deepened the pool exactly where models struggled most and are included in this open release. Thank you, LatchBio, for backing open, reproducible structural science.

Citation

OpenADMET PXR Structure Challenge — multi-model pose pool. Generated with Boltz-1/2, OpenFold3, AlphaFold3, Chai, Protenix v2, ESMFold2, RoseTTAFold3, apo templates. Released CC-BY-4.0.