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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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The Two-State Receptor Model01:29

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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
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Ligand Binding Sites02:40

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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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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Updated: Sep 11, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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DHAG-DTA: Dynamic Hierarchical Affinity Graph Model for Drug-Target Binding Affinity Prediction.

Cheng Wang, Yang Liu, Shitao Song

    IEEE Transactions on Computational Biology and Bioinformatics
    |August 14, 2025
    PubMed
    Summary
    This summary is machine-generated.

    We developed DHAG-DTA, a deep neural network, to predict drug-target binding affinity using molecular sequence data. This method achieves state-of-the-art performance, improving drug discovery efficiency.

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    Area of Science:

    • Computational chemistry
    • Bioinformatics
    • Drug discovery

    Background:

    • Drug-target binding affinity (DTA) prediction is crucial for identifying potential therapeutics.
    • Deep neural networks (DNNs) show promise for DTA prediction, especially when only sequence data is available.

    Purpose of the Study:

    • To propose DHAG-DTA, a novel dynamic hierarchical affinity graph DNN approach for DTA prediction.
    • To leverage molecular sequence information and known drug-target interactions for improved prediction accuracy.

    Main Methods:

    • DHAG-DTA utilizes a two-level hierarchical graph: an affinity graph for inter-molecular interactions and embedded molecular graphs for intra-molecular interactions.
    • Key innovations include a unified hierarchical graph, dynamic affinity graph structure determination, skip connections for information fusion, and robust feature embeddings for unseen molecules.

    Main Results:

    • DHAG-DTA demonstrated superior performance compared to existing models on two benchmark datasets.
    • The model achieved state-of-the-art results across multiple evaluation metrics.

    Conclusions:

    • DHAG-DTA effectively integrates inter- and intra-molecular interaction information for accurate DTA prediction.
    • The proposed method advances computational approaches in drug discovery and development.