Artificial Intelligence in the Pharmaceutical Industry: Transformative Applications, Challenges, and Future Perspectives

The pharmaceutical sector is dealing with a number of issues, such as growing R&D expenses, protracted drug development schedules, complicated regulations, supply chain interruptions, and heightened rivalry in the market. The development of new drugs is very resource intensive and often requires years of effort and billions of dollars as well as a high rate of clinical trial failures. The stringent regulatory requirements that demand extensive data validation and compliance further prolong the market entry process. There are also major issues related to counterfeit drugs and poor supply chain practice that are threats to both the business and the public. Artificial intelligence (AI) is one of the answers that can solve these problems with a change. AI-powered drug discovery cuts down on the time and expense involved with conventional methods by speeding up the identification of possible molecules. Machine learning algorithms can also help inform clinical trial designs and improve rates of successful trials by selecting the right patient population and predicting outcomes. AI-powered automation enhances compliance with regulations and ensures all documents are accurate and up to standard. AI can also help to predict demand, detect irregularities, and optimize logistics, minimizing the risk of shortages and counterfeit goods in manufacturing and supply chain management. Additionally, AI-powered personalized medicine reduces side effects and increases therapeutic efficacy by customizing therapies for each patient. Adoption of AI is hampered by issues including data protection, compatibility with existing systems, and governmental approval, despite its potential. With further advancements in AI, however, the pharmaceutical industry could benefit from increased involvement in addressing challenges, leading to improved patient outcomes, more efficient drug development, and better operational efficiency.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-26
DOI
https://doi.org/10.5281/zenodo.23656839
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Artificial Intelligence in the Pharmaceutical Industry: Transformative Applications, Challenges, and Future Perspectives

Anshuman Bhattacharya, Bhattacharyya Moulik, Mukherjee Payel
Zenodo (CERN European Organization for Nuclear Research)
Computational Drug Discovery Methods
article

Artificial Intelligence in the Pharmaceutical Industry: Transformative Applications, Challenges, and Future Perspectives

Anshuman Bhattacharya, Bhattacharyya Moulik, Mukherjee Payel
article en

Abstract

The pharmaceutical sector is dealing with a number of issues, such as growing R&D expenses, protracted drug development schedules, complicated regulations, supply chain interruptions, and heightened rivalry in the market. The development of new drugs is very resource intensive and often requires years of effort and billions of dollars as well as a high rate of clinical trial failures. The stringent regulatory requirements that demand extensive data validation and compliance further prolong the market entry process. There are also major issues related to counterfeit drugs and poor supply chain practice that are threats to both the business and the public. Artificial intelligence (AI) is one of the answers that can solve these problems with a change. AI-powered drug discovery cuts down on the time and expense involved with conventional methods by speeding up the identification of possible molecules. Machine learning algorithms can also help inform clinical trial designs and improve rates of successful trials by selecting the right patient population and predicting outcomes. AI-powered automation enhances compliance with regulations and ensures all documents are accurate and up to standard. AI can also help to predict demand, detect irregularities, and optimize logistics, minimizing the risk of shortages and counterfeit goods in manufacturing and supply chain management. Additionally, AI-powered personalized medicine reduces side effects and increases therapeutic efficacy by customizing therapies for each patient. Adoption of AI is hampered by issues including data protection, compatibility with existing systems, and governmental approval, despite its potential. With further advancements in AI, however, the pharmaceutical industry could benefit from increased involvement in addressing challenges, leading to improved patient outcomes, more efficient drug development, and better operational efficiency.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
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Artificial Intelligence in the Pharmaceutical Industry: Transformative Applications, Challenges, and Future Perspectives — Anshuman Bhattacharya, Bhattacharyya Moulik, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS