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<p>Identification of small-molecule binding sites in proteins is an important task for drug discovery. Despite previous homology- and machine-learning-based approaches to this problem, true de novo binding-site prediction remains a challenge. Here we use features from a pretrained neural network to train a logistic regression model, AF2BIND, for accurate prediction of de novo binding sites. AF2BIND identifies binding sites without relying on homology modeling, multiple sequence alignments or knowledge of a pocket-compatible ligand. Interpretable aspects of the model can be used to predict chemical properties of compatible ligands. We apply AF2BIND on the human proteome to produce a database that includes thousands of unseen binding sites in disease-relevant proteins. We anticipate AF2BIND will be used to focus drug discovery efforts and uncover functional sites in proteins across the tree of life.</p>

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AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月11日

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  • Artem Gazizov,
  • Anna Lian,
  • Casper Goverde,
  • Jody Mou,
  • Sergey Ovchinnikov,
  • Nicholas F. Polizzi

摘要

Identification of small-molecule binding sites in proteins is an important task for drug discovery. Despite previous homology- and machine-learning-based approaches to this problem, true de novo binding-site prediction remains a challenge. Here we use features from a pretrained neural network to train a logistic regression model, AF2BIND, for accurate prediction of de novo binding sites. AF2BIND identifies binding sites without relying on homology modeling, multiple sequence alignments or knowledge of a pocket-compatible ligand. Interpretable aspects of the model can be used to predict chemical properties of compatible ligands. We apply AF2BIND on the human proteome to produce a database that includes thousands of unseen binding sites in disease-relevant proteins. We anticipate AF2BIND will be used to focus drug discovery efforts and uncover functional sites in proteins across the tree of life.