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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Ligand Binding Sites02:40

Ligand Binding Sites

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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...

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  1. Home
  3. Multimodal Information-driven Heterogeneous Graph Neural Networks For Protein-ligand Binding Affinity Prediction.
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  3. Multimodal Information-driven Heterogeneous Graph Neural Networks For Protein-ligand Binding Affinity Prediction.

Related Experiment Video

Protein Target Prediction and Validation of Small Molecule Compound
10:21

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Multimodal Information-Driven Heterogeneous Graph Neural Networks for Protein-Ligand Binding Affinity Prediction.

Bin Yu1,2, Shiyue He2, Ya Li2

  • 1School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230027, China.

Journal of Chemical Information and Modeling
|June 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

AtomBind, a novel graph neural network framework, accurately predicts protein-ligand binding affinity (PLA) by integrating multimodal molecular data. This approach enhances drug screening efficiency through superior modeling of complex interactions.

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Related Experiment Videos

Protein Target Prediction and Validation of Small Molecule Compound
10:21

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Accurate prediction of protein-ligand binding affinity (PLA) is crucial for effective drug screening.
  • Existing methods struggle to adequately model the multimodal interactions influencing binding affinity.

Purpose of the Study:

  • Introduce AtomBind, a multimodal information-driven heterogeneous graph neural network framework.
  • Improve the accuracy and consistency of protein-ligand binding affinity prediction.

Main Methods:

  • Construct an atomic-level heterogeneous graph integrating sequence, 3D structure, and language model representations.
  • Employ Intra Encoder (variational graph autoencoder, equivariant graph neural network) for intra-molecular analysis.
  • Utilize Inter Encoder (graph diffusion convolution, graph Transformer) for inter-molecular interaction modeling.

Main Results:

  • AtomBind demonstrated superior predictive consistency and reduced errors on test sets compared to existing models.
  • Ablation studies confirmed the framework's efficiency and robustness in multimodal data integration.
  • Analysis validated the effectiveness of pretrained language models within the AtomBind framework.

Conclusions:

  • AtomBind offers a robust and efficient method for multimodal protein-ligand binding affinity prediction.
  • The model shows strong generalization capabilities and potential for practical applications in drug discovery.
  • AtomBind facilitates deeper analysis of protein pockets, intermolecular interactions, and model interpretability.