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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...
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:
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...
Drug-Receptor Bonds01:25

Drug-Receptor Bonds

Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
In...
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...

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Related Experiment Video

Updated: May 14, 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

Evaluating Molecular Docking Programs for RNA-Targeted Ligand Screening: Influence of Binding Modes and Ligand Types.

Kaichen Wang1,2, Louis DeFalco2, Chaitanya K Jaladanki2

  • 1Department of Pharmacy and Pharmaceutical Sciences, Faculty of Science, National University of Singapore, Block S4A, Level 3, 18 Science Drive 4, 117543, Singapore.

Journal of Chemical Information and Modeling
|May 12, 2026
PubMed
Summary

This study evaluates molecular docking programs for screening RNA targets, crucial for drug discovery. Consensus scoring across methods and structures improves performance for identifying potential RNA-binding drugs.

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Protein Target Prediction and Validation of Small Molecule Compound
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Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

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Last Updated: May 14, 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

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

Area of Science:

  • Computational biology
  • Drug discovery
  • Structural biology

Background:

  • Structured RNAs regulate gene expression and are key drug targets.
  • Molecular docking for RNA targets is underutilized due to RNA flexibility and limited methods.
  • Existing methods need evaluation for RNA-ligand docking efficacy.

Purpose of the Study:

  • To assess the performance of three widely used docking programs (Glide, GOLD, rDock) for RNA-ligand docking.
  • To explore consensus scoring strategies for improved virtual screening of RNA targets.
  • To provide practical guidance for structure-based virtual screening against structured RNAs.

Main Methods:

  • Evaluated Glide, GOLD, and rDock using a dataset of 25 RNA targets and 432 small-molecule binders.
  • Assessed performance across different RNA classes and docking programs.
  • Utilized consensus scoring across multiple receptor conformations and docking methods.
  • Performed clustering analysis of RNA binders to identify scoring function preferences.

Main Results:

  • Similar performance was observed across the evaluated docking methods.
  • Consistent performance differences were noted among distinct RNA classes.
  • Consensus scoring, averaging scores across programs and structures, demonstrated stable performance.
  • Clustering revealed specific scoring function preferences for different RNA binder chemotypes.

Conclusions:

  • Molecular docking programs show variable performance for RNA targets, influenced by RNA class.
  • Consensus scoring strategies offer a robust approach to enhance virtual screening accuracy.
  • Tailored scoring strategies based on binder chemotypes can optimize RNA-targeted drug discovery.
  • This study provides valuable insights and practical guidance for RNA-based drug discovery efforts.