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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-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...
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:
Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Updated: May 13, 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

Latest developments in molecular docking: 2010-2011 in review.

Elizabeth Yuriev1, Paul A Ramsland

  • 1Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia. Elizabeth.Yuriev@monash.edu

Journal of Molecular Recognition : JMR
|March 26, 2013
PubMed
Summary
This summary is machine-generated.

This review details advances in molecular docking, focusing on receptor flexibility and machine learning for drug design. Key developments enhance virtual screening and fragment-based drug design accuracy.

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Area of Science:

  • Computational chemistry and molecular modeling.
  • Drug discovery and pharmaceutical sciences.

Background:

  • Molecular docking predicts ligand-receptor interactions and binding affinity.
  • Accurate prediction is crucial for computational drug design.
  • The field has seen significant methodological advancements.

Purpose of the Study:

  • To review key developments in molecular docking from 2010-2011.
  • To focus on challenging aspects like receptor flexibility and scoring functions.
  • To highlight applications in virtual screening and fragment-based drug design.

Main Methods:

  • Review of literature on molecular docking methodologies.
  • Detailed discussion of advances in receptor flexibility and solvation.
  • Exploration of nonlinear scoring functions and machine learning approaches.

Main Results:

  • Significant progress in handling receptor flexibility and solvation effects.
  • Introduction of advanced machine learning techniques for scoring functions.
  • Improved accuracy in docking into homology models and fragment docking.

Conclusions:

  • Methodological advancements have enhanced the reliability of molecular docking.
  • These improvements are critical for effective virtual screening and fragment-based drug design.
  • Continued innovation in docking methods promises further acceleration of drug discovery.