Artificial intelligence and computational tools in drug repurposing: a new frontier in therapeutic discovery

Amtul Muqeet Rafia; Fathima Begum; Billa Yashaswi; C. Sai Manasa; Durgam Akshaya

doi:10.18203/2394-6040.ijcmph20264134

Authors

Amtul Muqeet Rafia Department of Pharmacy Practice, St. Pauls College of Pharmacy, Hyderabad, Telangana, India
Fathima Begum Department of Pharmacy Practice, St. Pauls College of Pharmacy, Hyderabad, Telangana, India
Billa Yashaswi Department of Pharmacy Practice, St. Pauls College of Pharmacy, Hyderabad, Telangana, India
C. Sai Manasa Department of Pharmacy Practice, St. Pauls College of Pharmacy, Hyderabad, Telangana, India
Durgam Akshaya Department of Pharmacy Practice, St. Pauls College of Pharmacy, Hyderabad, Telangana, India

DOI:

https://doi.org/10.18203/2394-6040.ijcmph20264134

Keywords:

Computational drug discovery, Artificial intelligence, Drug repurposing, Precision medicine, Deep learning, Machine learning

Abstract

Drug repurposing is the process of identifying novel therapeutic uses for currently available therapies that were initially created for different objectives in order to profit from the established safety and efficacy characteristics of well-known pharmaceuticals. Therefore, by avoiding the first phases of drug development, novel treatments can be introduced to the market more quickly and affordably. Artificial intelligence (AI) is a suitable substitute for conventional experimental approaches, which can be expensive and time-consuming, because of its lesser price, computer benefits, and capacity to reveal underlying trends. The primary subjects of this review include the use of AI algorithms in drug research and their unique and advantageous functions in identifying medicine repurposing, particularly when combined with in silico evaluation of therapeutic compounds. It has been shown that current AI systems are excellent at analyzing massive datasets, identifying intricate drug reaction patterns, and predicting potential medication repurposing. Building on these findings, there are still obstacles to establishing effective AI algorithms and carrying out further research, like expanding customized medicine, enhancing AI computing efficiency, and merging data-related databases for better repurposing.

Author Biography

Amtul Muqeet Rafia, Department of Pharmacy Practice, St. Pauls College of Pharmacy, Hyderabad, Telangana, India

Corresponding Author

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