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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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A Multi-perspective Model for Protein-Ligand-Binding Affinity Prediction.

Xianfeng Zhang1, Yafei Li2, Jinlan Wang3

  • 1School of Computer and Electronic Information, Nanjing Normal University, Nanjing, 210023, China.

Interdisciplinary Sciences, Computational Life Sciences
|October 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a general model to interpret multi-perspective graphs for protein-ligand-binding affinity prediction. The approach enhances predictive performance by abstractly representing protein-ligand complexes.

Keywords:
Binding affinity predictionData representationGraph neural networkProtein language model

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Area of Science:

  • Computational biology
  • Cheminformatics
  • Drug discovery

Background:

  • Accurate prediction of protein-ligand-binding affinity is crucial for drug discovery.
  • Traditional methods often analyze graph perspectives individually, leading to limited interpretability.
  • Heterogeneity of proteins and ligands presents a challenge in binding affinity prediction.

Purpose of the Study:

  • To develop a general model for harnessing multi-perspective graph information.
  • To improve the interpretability and predictive performance of protein-ligand-binding affinity prediction.
  • To address the heterogeneity of proteins and ligands in computational modeling.

Main Methods:

  • Utilizing multi-perspective graphs to represent protein-ligand complexes.
  • Developing a general abstract model for data representation.
  • Incorporating strategies to handle the heterogeneity of proteins and ligands.

Main Results:

  • The proposed model achieves excellent predictive performance in protein-ligand-binding affinity prediction.
  • The abstract representation strategy enhances model interpretability.
  • Experimental evaluations confirm the effectiveness of the data representation on public datasets.

Conclusions:

  • The developed general model effectively leverages multi-perspective graph information for improved protein-ligand-binding affinity prediction.
  • The approach offers enhanced interpretability and predictive accuracy, addressing key challenges in the field.
  • The study demonstrates a promising data representation strategy for computational drug discovery applications.