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<p>The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking methods or even to predict all-atom protein-ligand complex structures. Most contemporary studies focus on the accuracy and physical plausibility of ligand placement to determine pose quality, often neglecting a direct assessment of the interactions observed with the protein. In this work, we demonstrate that ignoring protein-ligand interaction fingerprints can lead to overestimation of model performance, most notably in recent protein-ligand cofolding models which often fail to recapitulate key interactions.</p><p><b>Scientific Contribution</b> The interaction analysis used in this study is provided as a python package at <a href="https://github.com/Exscientia/plif_validity">https://github.com/Exscientia/plif_validity</a>.</p>

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Assessing interaction recovery of predicted protein-ligand poses

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月19日

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  • David Errington,
  • Constantin Schneider,
  • Cédric Bouysset,
  • Frédéric A. Dreyer

摘要

The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking methods or even to predict all-atom protein-ligand complex structures. Most contemporary studies focus on the accuracy and physical plausibility of ligand placement to determine pose quality, often neglecting a direct assessment of the interactions observed with the protein. In this work, we demonstrate that ignoring protein-ligand interaction fingerprints can lead to overestimation of model performance, most notably in recent protein-ligand cofolding models which often fail to recapitulate key interactions.

Scientific Contribution The interaction analysis used in this study is provided as a python package at https://github.com/Exscientia/plif_validity.