โก Quick Summary
This review highlights the transformative role of Artificial Intelligence (AI) in drug discovery and translational medicine, emphasizing its ability to reduce costs, shorten development timelines, and enhance predictive capabilities. While AI presents significant opportunities, it also faces challenges such as data quality and ethical considerations.
๐ Key Details
- ๐ Study Scope: Systematic review of AI in drug discovery and translational medicine.
- ๐งฉ Key Terms: AI, drug discovery, machine learning, clinical trials, translational medicine.
- ๐ Publication Range: Peer-reviewed studies from 2014 to 2024.
- ๐ Data Extraction: Findings categorized by different phases of drug discovery.
๐ Key Takeaways
- ๐ก AI significantly lowers costs associated with drug development.
- โณ AI shortens the time required for bringing new drugs to market.
- ๐ฌ Enhanced predictive capabilities improve molecular modeling and drug design.
- ๐ฅ AI optimizes clinical trial design for better efficiency.
- ๐ Data quality issues remain a challenge for AI applications.
- โ๏ธ Ethical considerations must be addressed to ensure responsible AI use.
- ๐ AI’s role in healthcare is expected to grow, leading to better patient outcomes.
- ๐ฎ Future innovations in drug creation are likely to be stimulated by AI technologies.
๐ Background
The integration of Artificial Intelligence into drug discovery represents a significant shift in the pharmaceutical landscape. Traditional drug development processes are often lengthy and costly, making the need for innovative solutions more pressing than ever. AI technologies offer promising avenues to streamline these processes, enhance efficiency, and ultimately improve patient care.
๐๏ธ Study
This systematic review analyzed various peer-reviewed studies to assess the impact of AI on drug discovery and translational medicine. By focusing on studies published between 2014 and 2024, the authors aimed to provide a comprehensive overview of how AI is reshaping the drug development landscape.
๐ Results
The findings indicate that the application of AI in drug discovery can lead to significant cost reductions and shortened timelines for drug development. AI technologies have proven essential in areas such as molecular modeling, drug design, and the efficient design of clinical trials. However, challenges such as data quality, model interpretability, and ethical considerations remain critical issues that need to be addressed.
๐ Impact and Implications
The implications of AI in drug discovery are profound. By enhancing the efficiency and accuracy of drug development processes, AI has the potential to revolutionize healthcare. Improved predictive capabilities can lead to more effective treatments and better patient outcomes, ultimately transforming the landscape of medicine as we know it.
๐ฎ Conclusion
The review underscores the immense potential of AI to transform drug discovery and translational medicine. While challenges exist, the ongoing development and integration of AI technologies promise to enhance the efficiency of drug development processes and improve patient care. Continued research and collaboration will be essential to navigate the complexities of AI in healthcare.
๐ฌ Your comments
What are your thoughts on the role of AI in drug discovery? Do you see it as a game-changer for the pharmaceutical industry? Let’s engage in a discussion! ๐ฌ Share your insights in the comments below or connect with us on social media:
Transformative Role of Artificial Intelligence in Drug Discovery and Translational Medicine: Innovations, Challenges, and Future Prospects.
Abstract
INTRODUCTION: The use of Artificial intelligence in drug discovery is changing the field of Medicine across the world today positively. In this review, the role of AI in each focus area for the improvement of the drug development process, and its relevance in translational medicine is discussed.
MATERIALS AND METHOD: A systematic review was conducted by searching databases such as PubMed and Scopus, employing key terms like “AI” “drug discovery” “machine learning” “clinical trials” and “translational medicine.” Inclusion criteria focused on peer-reviewed studies published between 2014 and 2024 that specifically addressed the role of AI in drug development. Data extraction involved categorizing findings based on different phases of drug discovery.
RESULTS: The findings reveal that the use of AI lowers costs, shortens the time required for drug development, and enhances the predictive capability. AI technologies play an essential role in molecular modeling, drug design and screening, and the efficient design of clinical trials. However, some of the issues that remain include the quality of available data, issues of interpretability of the models, and the more critical issue of ethical considerations that need collective efforts on the development of associate regulatory policies.
CONCLUSION: AI holds immense potential to dramatically change and transform the process of drug discovery and translational medicine while promoting accurate prevention and cures. However, it is also important to understand how to work with existing problems to make the best use of AI in healthcare. The roles of AI technologies are likely to grow in the development of the medical future, provide patients with better results, and stimulate the innovations in the field of the drug creation.
Author: [‘Bassey GE’, ‘Daniel EA’, ‘Okesina KB’, ‘Odetayo AF’]
Journal: Drug Des Devel Ther
Citation: Bassey GE, et al. Transformative Role of Artificial Intelligence in Drug Discovery and Translational Medicine: Innovations, Challenges, and Future Prospects. Transformative Role of Artificial Intelligence in Drug Discovery and Translational Medicine: Innovations, Challenges, and Future Prospects. 2025; 19:7493-7502. doi: 10.2147/DDDT.S538269