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Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.

Derek Jones1, Hyojin Kim2, Xiaohua Zhang3

  • 1Global Security Computing Applications Division, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States.

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Fusion models combining deep learning approaches improve protein-ligand binding affinity prediction accuracy and efficiency. This study comprehensively compares three-dimensional convolutional neural networks (3D-CNNs) and spatial graph neural networks (SG-CNNs) against physics-based methods.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning in bioinformatics

Background:

  • Accurate prediction of protein-ligand binding affinities is crucial for drug discovery but remains challenging.
  • Existing methods, including physics-based scoring and deep learning, have limitations in accuracy and applicability.
  • The comparative advantages of different deep learning architectures (3D-CNNs, SG-CNNs) and their fusion are not well-established.

Purpose of the Study:

  • To develop and evaluate fusion models that integrate features from complementary representations for enhanced binding affinity prediction.
  • To conduct a comprehensive comparison of 3D-CNNs, SG-CNNs, and their fusion against traditional methods like docking scores and MM/GBSA.
  • To assess model performance under conditions where crystal structures are unavailable, using docking pose coordinates.

Main Methods:

  • Development of fusion models combining 3D-CNN and SG-CNN architectures.
  • Evaluation using temporal and structure-based data splits for novel protein targets.
  • Comparison with docking scores and MM/GBSA calculations, including predictions from docking pose coordinates.
  • Assessment of computational efficiency.

Main Results:

  • Fusion models demonstrated superior accuracy in predicting binding affinities compared to individual 3D-CNN and SG-CNN models.
  • The proposed fusion models outperformed traditional docking scores and MM/GBSA rescoring.
  • Fusion models offered greater computational efficiency than MM/GBSA methods.
  • Performance was validated even when crystal structures were not available.

Conclusions:

  • Fusion models integrating 3D-CNNs and SG-CNNs offer a significant advancement in predicting protein-ligand binding affinities.
  • These models provide a computationally efficient and accurate alternative to existing methods for drug discovery.
  • The study provides open-source code and trained models for reproducibility and practical application.