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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses 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...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
The Two-State Receptor Model01:29

The Two-State Receptor Model

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.
The binding affinity of a drug determines its interaction with one...
Protein-Drug Binding: Mechanism and Kinetics01:16

Protein-Drug Binding: Mechanism and Kinetics

Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
Various forces drive these interactions, including hydrogen bonds, hydrophobic interactions, ionic bonds, electrostatic interactions, and van der Waals forces. These bonds enable drugs to bind to specific sites on proteins,...

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Related Experiment Video

Updated: May 22, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

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

Published on: June 20, 2025

FAVAR-GAT: A Hybrid Temporal-structural Model for Drug-target Binding Affinity Prediction.

Madhuri Sharma1, Abhilasha Singh1

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Modinagar, Uttar Pradesh, 201204, India.

Current Drug Targets
|May 21, 2026
PubMed
Summary

This study introduces FAVAR-GAT, a hybrid AI framework that enhances drug-target binding affinity prediction by integrating latent factor modeling and graph attention networks for improved drug discovery.

Keywords:
Drug target interactionbinding affinity predictiondrug discoverymachine learning.protein–ligand interactions

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Last Updated: May 22, 2026

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

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Bioinformatics

Background:

  • Accurate drug-target binding affinity prediction is crucial for efficient drug discovery.
  • Traditional computational methods struggle with temporal dynamics and structural dependencies in protein-ligand interactions.
  • Limitations in predictive accuracy and stability hinder current approaches.

Purpose of the Study:

  • To develop a novel hybrid framework, FAVAR-GAT, to overcome limitations in drug-target binding affinity prediction.
  • To enhance the modeling of complex temporal and structural features in molecular interactions.
  • To improve the accuracy and stability of computational drug discovery.

Main Methods:

  • Proposed FAVAR-GAT, a hybrid framework combining Factor-Augmented Vector Autoregression (FAVAR) and Graph Attention Networks (GAT).
  • FAVAR extracts low-dimensional latent factors to model temporal interaction patterns.
  • GAT encodes molecular graph structures using adaptive attention to capture relational features.

Main Results:

  • FAVAR-GAT was evaluated on benchmark datasets (Davis, KIBA, BindingDB, PDBbind).
  • The framework demonstrated superior performance compared to state-of-the-art baseline models.
  • Improved predictive accuracy was shown across metrics like Concordance Index (CI), Mean Squared Error (MSE), and R².

Conclusions:

  • The hybrid FAVAR-GAT framework effectively combines latent factor modeling and attention-based graph learning.
  • This approach offers a robust and scalable solution for computational drug discovery.
  • The study highlights the potential for AI-driven pharmaceutical research and improved drug-target interaction analysis.