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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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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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AI-Based Drug Discovery and Design for Different Genetic Designs.

Devi Basumatary1,2, Shalini G Devi1,2, Deepsikha Swargiary1,3

  • 1Chemical Biology Lab-I, Institute of Advanced Study in Science and Technology (IASST), Guwahati, Assam, India.

Methods in Molecular Biology (Clifton, N.J.)
|June 24, 2025
PubMed
Summary

Artificial intelligence (AI) is revolutionizing personalized medicine by analyzing genetic data to tailor treatments. This approach enhances drug discovery and improves patient outcomes by identifying the most effective therapies.

Keywords:
Artificial intelligence (AI)Disease diagnosisDrug discoveryGenetic profilesPrecision medicines

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Area of Science:

  • Genomic Medicine
  • Computational Biology
  • Pharmacogenomics

Background:

  • Personalized medicine tailors treatments to individual genetic profiles, lifestyle, and environment.
  • This approach aims to maximize treatment efficacy and improve patient outcomes.
  • Traditional methods often involve trial-and-error in therapy selection.

Purpose of the Study:

  • To analyze the current state of AI-based drug development for personalized medicine.
  • To explore the application of AI in interpreting complex genomic data.
  • To identify challenges and strategies for leveraging AI in personalized medicine.

Main Methods:

  • Utilizing AI algorithms to analyze large-scale, diverse datasets, including genetic information.
  • Integrating and examining multiple data sources to uncover patterns and correlations.
  • Applying AI to interpret genomic data, such as DNA sequencing, to identify disease-associated genetic variants.

Main Results:

  • AI can identify genetic variants linked to disease susceptibility, drug response, and treatment outcomes.
  • AI algorithms uncover insights missed by conventional analytical techniques.
  • AI facilitates the development of personalized medicines tailored to individual genetic profiles.

Conclusions:

  • AI holds immense promise for revolutionizing personalized medicine.
  • AI-driven insights can optimize healthcare delivery and improve patient outcomes.
  • Further research is needed to fully realize AI's potential in personalized medicine and drug development.