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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2.

Artem Gazizov1, Anna Lian2,3, Casper Goverde4

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|March 12, 2026
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Summary

A new computational model, AF2BIND, accurately predicts novel protein binding sites for drug discovery. This method bypasses traditional techniques, enabling the identification of potential therapeutic targets in disease-relevant proteins.

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Area of Science:

  • Computational biology
  • Drug discovery
  • Structural bioinformatics

Background:

  • Identifying small-molecule protein binding sites is crucial for developing new drugs.
  • Existing methods like homology modeling and machine learning struggle with *de novo* prediction.
  • Predicting binding sites without prior ligand information or sequence alignments remains a significant challenge.

Purpose of the Study:

  • To develop an accurate *de novo* binding-site prediction method.
  • To create a tool that does not rely on homology or known ligand information.
  • To identify novel binding sites in proteins for drug discovery applications.

Main Methods:

  • Utilized features from a pretrained neural network.
  • Trained a logistic regression model named AF2BIND.
  • Applied AF2BIND to the human proteome.

Main Results:

  • AF2BIND accurately predicts *de novo* protein binding sites.
  • The model identifies sites without homology modeling or ligand knowledge.
  • A database of thousands of novel binding sites in disease-relevant proteins was generated.
  • Interpretable model features can predict ligand chemical properties.

Conclusions:

  • AF2BIND offers a novel approach to binding-site prediction.
  • The tool can accelerate drug discovery by identifying new targets.
  • AF2BIND has broad applicability for uncovering functional protein sites.