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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...

Ligand Binding Sites

Ligand Binding Sites

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A Fluorescence-based Lymphocyte Assay Suitable for High-throughput Screening of Small Molecules

A Fluorescence-based Lymphocyte Assay Suitable for High-throughput Screening of Small Molecules

Published on: March 10, 2017

LBVS: an online platform for ligand-based virtual screening using publicly accessible databases.

Minghao Zheng¹, Zhihong Liu, Xin Yan

¹Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, 132 East Circle at University City, Guangzhou, 510006, China.

Molecular Diversity

|September 4, 2014

Summary

This summary is machine-generated.

This study introduces a ligand-based virtual screening (LBVS) platform utilizing public databases and Bayesian learning. The LBVS platform demonstrates strong predictive accuracy for identifying drug leads.

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Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

Increasingly abundant compound bioactivity data and public chemical databases offer significant opportunities for ligand-based drug discovery.
Effective utilization of this data requires robust computational tools for virtual screening.

Purpose of the Study:

To develop and evaluate an online platform for ligand-based virtual screening (LBVS) that leverages publicly accessible chemical databases.
To assess the predictive performance of LBVS models for identifying potential drug leads.

Main Methods:

Development of an online LBVS platform integrating data from BindingDB and ChEMBL.
Application of a Bayesian learning approach to construct virtual screening models, chosen for noise tolerance, speed, and data-driven knowledge extraction.
Validation of LBVS models using tenfold cross-validation, internal testing, and external testing.

Main Results:

The LBVS platform achieved an average Area Under the Curve (AUC) value of 0.86 across twenty different models evaluated via tenfold cross-validation.
Internal and external testing confirmed the platform's predictive capability for lead identification.

Conclusions:

The developed LBVS platform effectively utilizes public data for ligand-based virtual screening.
LBVS demonstrates significant potential as a predictive tool for accelerating lead identification in drug discovery.
The platform is publicly accessible for broader research community use.