DRUG DEVELOPMENT THROUGH AI (Syllabus: GS Paper 3 – Sci and Tech)

News-CRUX-10     17th May 2024        

Context: Drug developers are seeking to speed up the testing and developing of new drugs with the help of artificial intelligence (AI).

Drug Development 

  • About: It is an expensive and time-consuming process. However, the advent of Artificial Intelligence (AI) has opened up a world of possibilities with respect to fast-tracking drug development.
  • Process of Developing Drug:

o Identifying and Validating the Target: The process begins with identifying and validating a target, typically a protein, which the drug directly binds to for its intended effect.

o Discovery Phase: In the discovery phase, target protein sequences are analyzed using computer algorithms to find suitable drug molecules from a library of small molecules.

o Analysis: Computational models are utilized to understand the binding sites between the target protein and potential drug molecules, streamlining the process and reducing reliance on expensive laboratory experiments.

o Clinical Trials: Following pre-clinical testing, the drug undergoes clinical trials where it is administered to a small number of human patients to evaluate efficacy and safety.

o Clinical trails in India: The Drugs Controller General of India (DCGI) is an official of the CDSCO who is the final regulatory authority for the approval of clinical trials in the country.

o Post-market Surveillance: Even after approval and marketing, the drug continues to be monitored through post-market surveillance to ensure its safety and effectiveness in real-world settings.


  • AI accelerates target discovery by reducing time through efficient prediction algorithms.
  • AI improves drug-target interaction understanding by enhancing prediction accuracy.
  • It offers cost-saving benefits by streamlining the drug development process
  • It refines the understanding of how drugs interact with their molecular targets, aiding in drug development.
  • AlphaFold and RoseTTAFold, based on deep neural networks, revolutionize computational drug development.
  • These tools leverage massive data inputs to accurately predict three-dimensional protein structures, facilitating drug design.
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