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<p>Accurate prediction of drug–target binding affinity plays an important role in structure-based drug discovery, yet existing approaches are constrained by their reliance on scarce experimentally determined protein–ligand complex structures. In this context, recent advances in biomolecular structure prediction models, such as AlphaFold3, have emerged as a promising approach to alleviate the scarcity of experimentally determined protein–ligand complex structures. In this study, we introduce AlphaDTA, a framework that integrates AlphaFold3-predicted structures and embeddings for drug–target binding affinity prediction. AlphaDTA processes two types of AlphaFold3 embeddings, single and pair embeddings. We utilized the single embeddings at both fine-grained and coarse-grained levels to capture local interaction patterns and global binding context, and used the pair embeddings to encode cross-molecular relational features. The AlphaFold3-predicted structures are further processed by a three-dimensional structure-based geometric encoder to produce corresponding structural embedding representations. The resulting single, pair, and structural embeddings are then integrated through adaptive fusion for affinity prediction. When we evaluated AlphaDTA using recently proposed PDBbind data splits designed to reduce train–test structural overlap, AlphaDTA achieved state-of-the-art or competitive performance across multiple independent benchmarks. In a case study on the cystic fibrosis transmembrane conductance regulator, AlphaDTA correctly identifies a clinically approved potentiator and suggests a potential repurposing candidate among FDA-approved drugs.</p><p><b>Scientific contribution</b></p><p> AlphaDTA enables accurate drug–target binding affinity prediction without relying on experimentally determined protein–ligand complex structures. By integrating AlphaFold3-derived single, pair, and structure-based embeddings through adaptive fusion, AlphaDTA improves generalization on structurally nonredundant benchmarks and demonstrates utility for target-specific drug repurposing.</p> Graphical Abstract <p></p>

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AlphaDTA: integrating AlphaFold3 embeddings and 3D complex structures for drug–target binding affinity prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Minjae Chung,
  • Sejin Park,
  • Hyunju Lee

摘要

Accurate prediction of drug–target binding affinity plays an important role in structure-based drug discovery, yet existing approaches are constrained by their reliance on scarce experimentally determined protein–ligand complex structures. In this context, recent advances in biomolecular structure prediction models, such as AlphaFold3, have emerged as a promising approach to alleviate the scarcity of experimentally determined protein–ligand complex structures. In this study, we introduce AlphaDTA, a framework that integrates AlphaFold3-predicted structures and embeddings for drug–target binding affinity prediction. AlphaDTA processes two types of AlphaFold3 embeddings, single and pair embeddings. We utilized the single embeddings at both fine-grained and coarse-grained levels to capture local interaction patterns and global binding context, and used the pair embeddings to encode cross-molecular relational features. The AlphaFold3-predicted structures are further processed by a three-dimensional structure-based geometric encoder to produce corresponding structural embedding representations. The resulting single, pair, and structural embeddings are then integrated through adaptive fusion for affinity prediction. When we evaluated AlphaDTA using recently proposed PDBbind data splits designed to reduce train–test structural overlap, AlphaDTA achieved state-of-the-art or competitive performance across multiple independent benchmarks. In a case study on the cystic fibrosis transmembrane conductance regulator, AlphaDTA correctly identifies a clinically approved potentiator and suggests a potential repurposing candidate among FDA-approved drugs.

Scientific contribution

AlphaDTA enables accurate drug–target binding affinity prediction without relying on experimentally determined protein–ligand complex structures. By integrating AlphaFold3-derived single, pair, and structure-based embeddings through adaptive fusion, AlphaDTA improves generalization on structurally nonredundant benchmarks and demonstrates utility for target-specific drug repurposing.

Graphical Abstract