Recent Patents on Biotechnology, volume 18, issue 1, pages 35-52

Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications

Pranjal Kumar Singh 1
KAPIL SACHAN 2
Vishal Khandelwal 3
Sumita Singh 4
Smita Singh 5
1
 
Department of Pharmacy, Kalka Institute for Research and Advanced Studies, Meerut, Uttar Pradesh, India
2
 
KIET School of Pharmacy, KIET Group of Institutions, Ghaziabad, Uttar Pradesh, India
4
 
Faculty of Pharmacy, Swami Vivekanand Subharti University, Meerut, Uttar Pradesh, India
Publication typeJournal Article
Publication date2025-03-01
scimago Q3
SJR0.277
CiteScore2.9
Impact factor
ISSN18722083, 22124012
Applied Microbiology and Biotechnology
Biotechnology
Bioengineering
Abstract

Traditional drug discovery methods such as wet-lab testing, validations, and synthetic techniques are time-consuming and expensive. Artificial Intelligence (AI) approaches have progressed to the point where they can have a significant impact on the drug discovery process. Using massive volumes of open data, artificial intelligence methods are revolutionizing the pharmaceutical industry. In the last few decades, many AI-based models have been developed and implemented in many areas of the drug development process. These models have been used as a supplement to conventional research to uncover superior pharmaceuticals expeditiously. AI's involvement in the pharmaceutical industry was used mostly for reverse engineering of existing patents and the invention of new synthesis pathways. Drug research and development to repurposing and productivity benefits in the pharmaceutical business through clinical trials. AI is studied in this article for its numerous potential uses. We have discussed how AI can be put to use in the pharmaceutical sector, specifically for predicting a drug's toxicity, bioactivity, and physicochemical characteristics, among other things. In this review article, we have discussed its application to a variety of problems, including <i>de novo</i> drug discovery, target structure prediction, interaction prediction, and binding affinity prediction. AI for predicting drug interactions and nanomedicines were also considered.

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