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CGDock enhances drug discovery by accurately predicting protein-ligand binding poses using curvature-aware geometric flows. This novel framework improves pocket identification and binding predictions for therapeutic development.

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Area of Science:

  • Computational chemistry and structural biology
  • Drug discovery and medicinal chemistry
  • Machine learning in bioinformatics

Background:

  • Accurate prediction of small-molecule ligand conformations to protein targets is crucial for drug discovery.
  • Existing deep learning docking frameworks often neglect essential local geometric features, limiting pocket identification precision and binding pose reliability.
  • Current pocket prediction methods lack adaptability due to reliance on external tools or fixed thresholds.

Purpose of the Study:

  • To introduce CGDock, an end-to-end protein-ligand docking framework utilizing curvature-aware geometric flows for enhanced discrete structural representation learning.
  • To improve the accuracy of binding pose predictions by integrating local geometric features and employing a ligand-guided adaptive pocket prediction module.
  • To develop a unified architecture that simplifies the docking workflow and accommodates protein structural heterogeneity.

Main Methods:

  • Integration of discrete Ricci curvature into molecular graph representations to strengthen local structural feature encoding for proteins and ligands.
  • Implementation of a ligand-guided adaptive pocket prediction module for estimating ligand-specific binding regions.
  • A unified architecture for iterative geometric optimization, refining protein-ligand conformation after pocket identification.

Main Results:

  • CGDock demonstrates competitive performance on the PDBind v2020 dataset for protein-ligand docking.
  • The framework successfully integrates local geometric features, improving pocket identification and binding pose prediction accuracy.
  • The curvature-aware geometric flow operator proves effective as a plug-and-play geometric descriptor.

Conclusions:

  • CGDock offers a significant advancement in protein-ligand docking by incorporating curvature-aware geometric flows and adaptive pocket prediction.
  • The framework provides accurate binding pose predictions and simplifies the drug discovery workflow.
  • The developed geometric descriptor has potential for broader applications in protein-ligand interaction modeling.