Artificial intelligence in drug discovery: Current applications and future directions

Ahmad Asri Awad Alanazi; ‏Abdulrahman Ibrahim Abdullah Al Fahad; Abdullah Saleh Abdullah Almorshed; ‏Abdullah Abdulrahman Mohammed Alrbian; Ahmed Abdulkarim Sulaiman Alnughaymishi; Nawal Haif Bajad Al-Mutairi; Abdulrahman Amer Alajmi; Sanytan Ghazy Al Otaibi

doi:10.53730/ijhs.v6nS10.15290

Authors

Ahmad Asri Awad Alanazi ‏Health Informatics technician, ‏National Guard Health Affairs
‏Abdulrahman Ibrahim Abdullah Al Fahad ‏Health Informatics technician, ‏National Guard Health Affairs
Abdullah Saleh Abdullah Almorshed ‏Pharmacy Technician, National Guard Health Affairs
‏Abdullah Abdulrahman Mohammed Alrbian ‏Pharmacy Technician, National Guard Health Affairs
Ahmed Abdulkarim Sulaiman Alnughaymishi ‏Health Informatics technician, ‏National Guard Health Affairs
Nawal Haif Bajad Al-Mutairi ‏National Guard Health Affairs
Abdulrahman Amer Alajmi ‏National Guard Health Affairs
Sanytan Ghazy Al Otaibi ‏National Guard Health Affairs

Keywords:

Artificial Intelligence, Drug Discovery, Machine Learning, Predictive Modeling, Pharmaceutical Industry

Abstract

Background: The drug discovery process is complex, time-consuming, and costly, traditionally relying on trial-and-error approaches. The integration of artificial intelligence (AI) and machine learning (ML) has emerged as a transformative solution, enhancing efficiency and precision in identifying potential drug candidates. Aim: This review aims to explore the current applications of AI in drug discovery, highlight the AI tools utilized in the process, and discuss the associated challenges. Methods: A comprehensive literature review was conducted, focusing on peer-reviewed articles, clinical studies, and case reports that detail the application of AI and ML in various phases of drug discovery, including target identification, lead optimization, and preclinical evaluation. Results: The review identifies several AI applications, such as predictive modeling, molecular design, and virtual screening, which significantly expedite the discovery process. Tools such as deep learning, natural language processing, and reinforcement learning have been instrumental in analyzing large datasets and predicting drug interactions. However, challenges remain, including data integration issues, skill gaps among professionals, and skepticism regarding AI outcomes. Conclusion: AI has the potential to revolutionize drug discovery by streamlining processes and improving accuracy.

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