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Ligand Binding Sites

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 Strength

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 Linkage

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...

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Related Experiment Video

Updated: Jun 29, 2025

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

Published on: June 4, 2021

FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening.

Haihan Liu¹, Peiying Chen¹, Baichun Hu¹

¹Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; Key Laboratory of Intelligent Drug Design and New Drug Discovery of Liaoning Province, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.

Computational Biology and Chemistry

|April 6, 2024

Summary

This summary is machine-generated.

FaissMolLib is a new open-source tool for virtual screening that uses the Faiss algorithm to rapidly search large compound databases. It significantly speeds up drug discovery by efficiently identifying similar molecules.

Keywords:

Database Drug design Molecular fingerprint Virtual screening

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Last Updated: Jun 29, 2025

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

Published on: June 4, 2021

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

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Published on: May 16, 2021

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

Molecular similarity and fingerprinting are vital for drug design, target prediction, and ADMET prediction.
Existing challenges include a lack of comprehensive open-source molecular fingerprint databases and efficient virtual screening search methods.

Purpose of the Study:

To introduce FaissMolLib, an open-source virtual screening tool designed to overcome current limitations.
To provide an efficient and easily deployable solution for virtual screening and compound database creation.

Main Methods:

FaissMolLib integrates 2.8 million compounds from the ChEMBL and ZINC databases.
It employs the highly efficient Faiss search algorithm for molecular similarity searches.
Performance is compared against the Tanimoto algorithm.

Main Results:

FaissMolLib demonstrates superior performance in identifying similar molecules compared to the Tanimoto algorithm.
The Faiss algorithm results in tighter clustering and lower variance in molecular properties.
FaissMolLib screens 2.8 million compounds in 0.05 seconds, significantly faster than traditional methods.

Conclusions:

FaissMolLib offers an efficient, laptop-deployable solution for virtual screening and building unique compound databases.
This tool has the potential to accelerate drug discovery and enhance chemical data analysis.
FaissMolLib is freely available, promoting open science and accessibility.