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An image-based protein-ligand binding representation learning framework via multi-level flexible dynamics trajectory

Hongxin Xiang1, Mingquan Liu1, Linlin Hou1

  • 1College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China.

Bioinformatics (Oxford, England)
|September 24, 2025
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Summary
This summary is machine-generated.

ImagePLB is a novel framework for learning protein-ligand binding (PLB) representations using 3D ligand images and protein graphs. It achieves competitive improvements in PLB prediction tasks, advancing drug discovery.

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Area of Science:

  • Computational chemistry and cheminformatics
  • Structural biology and bioinformatics
  • Drug discovery and medicinal chemistry

Background:

  • Accurate prediction of protein-ligand binding (PLB) relationships is vital for drug discovery, aiding in the identification of drugs targeting specific proteins.
  • Traditional experimental methods for measuring PLB are time-consuming and expensive.
  • Existing computational models for PLB prediction require more accurate representations to meet drug discovery standards.

Purpose of the Study:

  • To develop an advanced image-based framework for learning protein-ligand binding representations.
  • To improve the accuracy and efficiency of predicting protein-ligand interactions for drug discovery applications.
  • To introduce a novel pre-training strategy to enhance the learning of interaction information.

Main Methods:

  • Proposed ImagePLB, an image-based framework with ligand representation learner (LRL) and protein representation learner (PRL) accepting 3D ligand images and protein graphs.
  • Introduced a multi-level next trajectory prediction (MLNTP) task to pre-train ImagePLB on 4D flexible dynamics trajectories of 16,972 complexes.
  • Incorporated trajectory regularization (TR) to mitigate feature similarity issues from adjacent trajectories.

Main Results:

  • ImagePLB demonstrated competitive improvements on protein-ligand affinity and efficacy prediction tasks compared to state-of-the-art methods.
  • The MLNTP pre-training task effectively learned trajectory-related information at ligand, protein, and complex levels.
  • TR successfully addressed high feature similarity issues caused by adjacent trajectories.

Conclusions:

  • ImagePLB offers a promising new paradigm for image-based protein-ligand binding learning.
  • The framework enhances the accuracy of PLB predictions, supporting more efficient drug discovery.
  • This approach paves the way for future advancements in computational drug design and development.