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Drug Discovery: Overview

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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Related Experiment Video

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High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Published on: March 3, 2015

How polypharmacologic is each chemogenomics library?

Eric Ni^1,2,3, Eehjoe Kwon¹, Lauren M Young²

¹NYU Langone Health, Department of Biochemistry & Molecular Pharmacology, New York, NY 10016, USA.

Future Drug Discovery

|March 10, 2020

Summary

This summary is machine-generated.

Chemogenomics libraries aid drug discovery by helping identify small molecule targets. A new polypharmacology index reveals which libraries are most target-specific for effective use in phenotypic screens.

Keywords:

chemogenomics polypharmacology target deconvolution

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Published on: May 21, 2018

Area of Science:

Medicinal Chemistry
Pharmacology
Drug Discovery

Background:

High-throughput phenotypic screens are crucial for identifying potential drug candidates.
A major challenge in phenotypic screening is determining the specific molecular target of a hit compound.
Chemogenomics libraries are increasingly used to address the target deconvolution problem.

Purpose of the Study:

To evaluate the target-specificity of various chemogenomics screening libraries.
To introduce a quantitative 'polypharmacology index' for assessing library utility.
To identify the most suitable libraries for target deconvolution in phenotypic screens.

Main Methods:

Compiled known target information for all compounds within selected chemogenomics libraries.
Modeled the distribution of compound targets using a Boltzmann distribution histogram.
Derived a linearized slope from the Boltzmann fit to represent the polypharmacology index.

Main Results:

The polypharmacology index effectively quantified the target-specificity of different libraries.
Distinct differences in target-specificity were observed among the screened libraries.
One library was identified as significantly more target-specific than others.

Conclusions:

The polypharmacology index provides a valuable metric for assessing chemogenomics libraries.
The most target-specific library identified is likely superior for target deconvolution.
This work aids in selecting optimal libraries for advancing small-molecule drug discovery.