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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Enzyme-linked receptors are cell-surface receptors acting as an enzyme or associating with an enzyme intracellularly. They make excellent drug targets. Drugs can bind to the extracellular ligand-binding domain or directly affect their enzymatic domain and alter their activity.
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Related Experiment Video

Updated: Jul 22, 2025

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
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AI-powered therapeutic target discovery.

Frank W Pun1, Ivan V Ozerov1, Alex Zhavoronkov2

  • 1Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong.

Trends in Pharmacological Sciences
|July 21, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) accelerates drug discovery by improving disease modeling and target identification. AI-driven approaches are leading to validated targets and drugs entering clinical trials.

Keywords:
artificial intelligencedeep learningdrug discoverymultiomicsnoveltytarget identification

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Area of Science:

  • Drug discovery and development
  • Computational biology
  • Bioinformatics

Background:

  • Target identification is critical for drug discovery but traditionally time-consuming.
  • Artificial intelligence (AI) offers powerful tools for analyzing complex biological data.
  • AI is increasingly vital for efficient and effective drug target exploration.

Purpose of the Study:

  • To review recent advancements in AI-driven therapeutic target discovery.
  • To discuss the balance between novelty and confidence in selecting drug targets.
  • To highlight limitations and future directions in AI for drug development.

Main Methods:

  • Review of recent literature on AI applications in drug target identification.
  • Analysis of AI's role in disease modeling and biological network analysis.
  • Examination of AI-identified targets progressing towards clinical validation.

Main Results:

  • AI significantly speeds up the traditionally slow process of target identification.
  • Numerous AI-identified targets are undergoing experimental validation.
  • Several drugs derived from AI-identified targets are now in clinical trials.

Conclusions:

  • AI is revolutionizing drug discovery by enhancing target identification and validation.
  • Balancing novel targets with robust validation is key for AI-driven success.
  • Addressing current limitations will further unlock AI's potential in pharmaceutical development.