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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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Updated: May 26, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Target discovery from data mining approaches.

Yongliang Yang¹, S James Adelstein, Amin I Kassis

¹Harvard Medical School, Harvard University, Department of Radiology, 200 Longwood Avenue, Boston, MA 02115, USA. everbright99@gmail.com

Drug Discovery Today

|December 20, 2011

Summary

This summary is machine-generated.

Data mining accelerates target discovery for biomarkers and drugs in the

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Area of Science:

Biomedical Science
Bioinformatics
Computational Biology

Background:

Target discovery is crucial for biomarker and drug development in the 'omics' era.
A 'target' encompasses molecular entities (genes, proteins) and biological phenomena (pathways, phenotypes).
Data mining in biomedicine integrates biological concepts with computational tools for target identification.

Purpose of the Study:

To review data mining approaches for target discovery in the 'omics' era.
To emphasize text and microarray data analysis applications.
To introduce emerging chemogenomic and proteomic data mining methods.

Main Methods:

Review of existing literature on data mining techniques in biomedical target discovery.
Emphasis on text mining and microarray data analysis.
Brief introduction to chemogenomic and proteomic data mining.

Main Results:

Data mining significantly enhances target discovery for diagnosing and treating diseases.
Text and microarray data analysis are key applications.
Emerging methods like chemogenomic and proteomic data mining show promise.

Conclusions:

Data mining is essential for advancing target discovery in 'omics' research.
Limitations include database integration, data quality, and tool performance.
Integrating diverse data sources, like text mining with high-throughput data and pathway databases, offers promising strategies.