From Dataset Curation to Unified Evaluation: Revisiting Structure Prediction Benchmarks with PXMeter

Ma, W.; Liu, Z.; Yang, J.; Lu, C.; Zhang, H.; Xiao, W.

doi:10.1101/2025.07.17.664878

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bioRxiv · 10.1101/2025.07.17.664878

From Dataset Curation to Unified Evaluation: Revisiting Structure Prediction Benchmarks with PXMeter

Ma, W.·Liu, Z.·Yang, J.·Lu, C.·Zhang, H.·Xiao, W.

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Abstract

Recent advances in deep learning have significantly improved the accuracy of structure prediction for biomolecular complexes; however, robust evaluation of these models remains a major challenge. We introduce PXMeter, an open-source toolkit that support consistent and reproducible evaluation of diverse predictive models across a broad spectrum of biological complex structures. PXMeter provides a unified and reproducible benchmarking framework, offering valuable insights to support the ongoing improvement of structure prediction methods. We also present a high-quality benchmark dataset curated from recently deposited structures in the Protein Data Bank (PDB). These entries are manually reviewed to exclude non-biological interactions, ensuring reliable evaluation. Using these resources, we conducted a comprehensive benchmark of several structure prediction models, namely Chai-1, Boltz-1, and Protenix. Our benchmarking results demonstrate the advancements achieved by deep learning models, while also identifying ongoing challenges--especially in modeling protein-protein and protein-RNA interactions. Project Pagehttps://github.com/bytedance/PXMeter

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Ma, W., Liu, Z., Yang, J., Lu, C., Zhang, H., Xiao, W.. 2025-07-22. From Dataset Curation to Unified Evaluation: Revisiting Structure Prediction Benchmarks with PXMeter. https://doi.org/10.1101/2025.07.17.664878

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