Run PXMeter metrics (LDDT, DockQ, RMSD, PoseBusters) against reference CIFs and output JSON/Parquet.
npx skills add https://github.com/bytedance/PXMeter --skill pxmeter
Please help me install this Agent Skill. Skill: pxmeter Source: https://github.com/bytedance/PXMeter/tree/main Command: npx skills add https://github.com/bytedance/PXMeter --skill pxmeter
PXMeter provides a scalable, end-to-end evaluation workflow for biomolecular structure predictions, enabling rigorous, multi-metric quality assessment against reference structures.
Invoke PXMeter to evaluate CIF predictions and interpret the resulting JSON/Parquet outputs, using either the CLI or Python API.
High-intent search queries and answers about installing and using this skill.
Biomolecular structure evaluation computes LDDT, DockQ, pocket RMSD, and PoseBusters metrics against reference CIFs. This Skill automates PXMeter evaluation for both single-sample checks and large-scale benchmark datasets, outputting parsed JSON or Parquet files.
Yes, large-scale protein structure benchmarks are supported. You can compare performance across multiple prediction tools and datasets by running PXMeter metrics via CLI or Python API, enabling rapid automated evaluation of extensive CIF prediction sets.
Automating PoseBusters validity checks requires a PXMeter setup with CLI or Python API access. The workflow evaluates CIF predictions against reference structures and outputs parsed results in JSON or Parquet formats for downstream analysis.
RMSD and LDDT structure evaluation requires CIF format files for both predictions and reference structures. The workflow parses these inputs to compute multi-metric quality assessments and exports the evaluated results as JSON or Parquet outputs.
Yes, PXMeter supports structure evaluation across proteins, nucleic acids, and ligands. It computes LDDT, DockQ, pocket RMSD, and PoseBusters validity metrics for comprehensive biomolecular quality assessment.
The best way to parse PXMeter structure evaluation results is using the Python API or CLI. The workflow automatically processes computed metrics and exports parsed outputs in JSON or Parquet formats for immediate downstream analysis.
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