mlsanityData to 7 Oct 2026

Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling

arXiv 2209.13014 ↗First posted 18 Sep 2022q-bio.BM cs.AI cs.LGcited by 5

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Abstract (arXiv)

The prediction of protein-ligand binding affinity is of great significance for discovering lead compounds in drug research. Facing this challenging task, most existing prediction methods rely on the topological and/or spatial structure of molecules and the local interactions while ignoring the multi-level inter-molecular interactions between proteins and ligands, which often lead to sub-optimal performance. To solve this issue, we propose a novel global-local interaction (GLI) framework to predict protein-ligand binding affinity. In particular, our GLI framework considers the inter-molecular interactions between proteins and ligands, which involve not only the high-energy short-range interactions between closed atoms but also the low-energy long-range interactions between non-bonded atoms. For each pair of protein and ligand, our GLI embeds the long-range interactions globally and aggregates local short-range interactions, respectively. Such a joint global-local interaction modeling strategy helps to improve prediction accuracy, and the whole framework is compatible with various neural network-based modules. Experiments demonstrate that our GLI framework outperforms state-of-the-art methods with simple neural network architectures and moderate computational costs.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 19 of its 54 references that are in the map. First posted on arXiv 18 Sep 2022.

Builds on

19 of 54 references.

References · 19

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7 more
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Cited by year

Citing papers per year: 2023: 4; 2024: 0; 2025: 1.

5 papers.

  1. 2023EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Predictioncited by 15
  2. 2023Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Predictioncited by 7
  3. 2023Geometric Deep Learning for Structure-Based Drug Design: A Surveycited by 2
  4. 2023ETDock: A Novel Equivariant Transformer for Protein-Ligand Dockingcited by 2
  5. 2025Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Reviewcited by 2