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Related Concept Videos

Drug Discovery: Overview01:26

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...
Structure-Activity Relationships and Drug Design01:28

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...

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Related Experiment Video

Updated: May 13, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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Published on: May 27, 2021

Integrating scientific data for drug discovery and development using the Life Sciences Grid.

Ernst R Dow1, James B Hughes, Susie M Stephens

  • 1Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA +1 317 433 5814 ; +1 317 276 4127 ; dow@lilly.com.

Expert Opinion on Drug Discovery
|March 16, 2013
PubMed
Summary

The Life Science Grid integrates complex biological and chemical data, aiding pharmaceutical companies in developing better-understood drugs and overcoming market challenges. This open-source platform fosters collaboration for drug discovery.

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Area of Science:

  • Pharmaceutical Science
  • Bioinformatics
  • Computational Biology

Background:

  • Drug development faces challenges from increased data complexity and higher market approval standards.
  • Novel drugs must demonstrate superior performance against generics, with regulatory scrutiny on safety impacting approvals.
  • Understanding disease mechanisms and identifying drug targets requires integrating diverse data sources.

Purpose of the Study:

  • To address challenges in drug development by leveraging information technology for better-understood compounds.
  • To facilitate the identification of physiological targets for unmet medical needs.
  • To integrate heterogeneous data for informed decisions in target selection.

Main Methods:

  • Development of the Life Science Grid as a flexible framework.
  • Integration of diverse biological, chemical, and disease-related information.
  • Utilizing the platform to support scientific decision-making in drug discovery.

Main Results:

  • The Life Science Grid successfully integrated scientific information within the pharmaceutical industry.
  • The platform demonstrated rapid and effective data integration capabilities.
  • The Life Science Grid was released into the open-source community.

Conclusions:

  • The Life Science Grid facilitates informed decision-making in pharmaceutical research and development.
  • Open-sourcing the Life Science Grid promotes collaboration across the life sciences community.
  • The platform aids in overcoming drug development hurdles by improving data integration and understanding.