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Drug Discovery: Overview01:26

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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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Artificial intelligence revolutionizing CNS drug discovery and development.

Md E K Talukder1, Mohammad R Islam1, Saghir Ali2

  • 1School of Basic Pharmaceutical and Toxicological Sciences, College of Pharmacy, The University of Louisiana Monroe, Monroe, LA 71201, USA.

Drug Discovery Today
|April 12, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and computer-aided drug design (CADD) are revolutionizing central nervous system (CNS) drug discovery. These technologies offer precise, scalable approaches to overcome challenges in developing treatments for neurological diseases.

Keywords:
artificial intelligencecentral nervous systemcomputer-aided drug designdeep learningmachine learningmedicinal chemistryneurodegenerative diseases

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Area of Science:

  • Neuroscience
  • Pharmacology
  • Computational Chemistry

Background:

  • Central nervous system (CNS) drug discovery is hindered by high attrition rates, long development timelines, and significant costs.
  • Complex neurological disease biology and challenges in safe drug delivery contribute to these difficulties.
  • Traditional CNS drug discovery methods are slow and rely on trial-and-error, often leading to poor brain penetration, toxicity, or limited efficacy.

Purpose of the Study:

  • To review the transformative impact of artificial intelligence (AI) on early-stage central nervous system (CNS) drug discovery.
  • To highlight how AI and computer-aided drug design (CADD) are addressing the limitations of conventional drug development processes.
  • To explore the application of AI in developing therapeutics for complex and underserved neurological diseases.

Main Methods:

  • Integration of artificial intelligence (AI) with computer-aided drug design (CADD) principles.
  • Utilizing AI-powered tools for prioritizing high-value drug analogs.
  • Streamlining the design and optimization phases of therapeutic development through AI.

Main Results:

  • AI enables more precise and scalable approaches in CNS drug discovery.
  • AI tools enhance the efficiency of identifying and optimizing potential drug candidates.
  • The review outlines the potential of AI to redefine early-stage therapeutic development for neurological disorders.

Conclusions:

  • AI and CADD represent a paradigm shift in addressing the challenges of CNS drug discovery.
  • These integrated technologies promise to accelerate the development of effective treatments for neurological conditions.
  • AI offers a powerful strategy for tackling complex and historically underserved neurological diseases.