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Artificial Intelligence in Drug Identification and Validation: A Scoping Review.
Mukhtar Lawal Abubakar1, Neha Kapoor1, Asha Sharma2
1School of Applied Sciences, Suresh Gyan Vihar University, Jaipur, Rajasthan, India.
Drug Research
|June 3, 2024
Summary
Artificial intelligence (AI) is improving drug discovery by streamlining the lengthy, expensive, and inefficient process. This study reviews AI applications in drug discovery, identifies research gaps, and suggests future opportunities.
Area of Science:
- Computational chemistry
- Pharmacology
- Biotechnology
Background:
- Drug discovery is a complex, lengthy, and costly process with high attrition rates.
- Improving efficiency and reducing time and cost are critical for the pharmaceutical industry.
- Artificial intelligence (AI) offers potential solutions to enhance various stages of drug discovery.
Purpose of the Study:
- To review recent AI-based models in drug discovery.
- To identify stages requiring further attention.
- To categorize AI methods and propose future research directions.
Main Methods:
- Systematic literature search across multiple electronic databases (Scopus, PubMed, MEDLINE, etc.) from January 2016 to September 2023.
- Data extraction using a standardized form.
- Analysis of extracted data to identify trends and research opportunities.
Main Results:
- Identification of numerous AI applications across different drug discovery stages.
- Highlighting specific stages that would benefit from increased AI focus.
- Development of a taxonomy for AI methods used in drug discovery.
Conclusions:
- AI is a transformative technology in drug discovery, offering significant improvements in efficiency and success rates.
- Further research is needed in specific areas to fully leverage AI's potential.
- The study provides a roadmap for future AI-driven drug discovery research.
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