AI Application in the Creation of Medications for COPD

Year : 2025 | Volume : 12 | Issue : 02 | Page : 01 05
    By

    Ramandeep Kaur,

  • Prabhsharan Kaur,

  • Rohit Mittal,

  1. Research Scholar, Department of Pharmaceutical Sciences, Guru Kashi University, Bathinda, Punjab, India
  2. Research Scholar, Department of Pharmaceutical Sciences, Guru Kashi University, Bathinda, Punjab, India
  3. Assistant Professor, Department of Pharmaceutical Sciences, Guru Kashi University, Bathinda, Punjab, India

Abstract

The crippling lung condition known as chronic obstructive pulmonary disease (COPD) is typified by a continuous restriction of airflow, which results in increased respiratory dysfunction and a reduced quality of life. The rising incidence of COPD worldwide emphasizes the pressing need for innovative pharmaceutical approaches to address the illness. Even though COPD care has advanced significantly, most current medications concentrate on symptom relief rather than disease change. This gap in therapeutic efficacy necessitates the exploration of innovative approaches in drug discovery, with artificial intelligence (AI) emerging as a transformative technology. AI can process large quantities of complex biological data, make predictions, and optimize drug development processes is increasingly being applied to COPD drug discovery. AI technologies, including natural language processing, and deep learning, machine learning, are being integrated at various stages of drug discovery and development for COPD. These tools are used to find out new drug targets, design and optimize drug molecules, predict patient responses, and accelerate clinical trial design. Machine learning algorithms, for instance, can analyze genetic, proteomic, and clinical data to uncover novel biomarkers and disease mechanisms, potentially leading to the identification of new therapeutic targets for COPD. Additionally, AI-powered systems can predict the pharmacological properties of drug candidates and optimize them for better efficacy and safety profiles. In drug screening, AI methods are being used to predict the interaction of drug molecules with COPD-related proteins and predict potential adverse effects, decreasing the cost and time associated with traditional high-throughput screening techniques. AI’s application in clinical trials is also noteworthy, with algorithms being utilized to optimize trial design, predict patient responses based on genetic information, and streamline patient recruitment processes, leading to more efficient trials. Furthermore, AI is contributing to post-market surveillance by detecting and predicting long-term adverse effects and monitoring drug safety in real-world settings. Even though AI has a lot of techniques for COPD medication development, issues like data quality, model interpretability, and regulatory barriers, still exist. Despite these challenges, AI technologies are expected to play a pivotal role in the future of COPD treatment, enabling the development of more personalized, targeted and effective therapies. This review discusses the current landscape of AI in COPD drug discovery, exploring its applications, benefits, challenges, and prospects in the discovery and development of novel treatments for COPD.

Keywords: COPD, drug screening, clinical trials, Drug Design, Artificial Intelligence.

[This article belongs to Research & Reviews: A Journal of Drug Design & Discovery ]

How to cite this article:
Ramandeep Kaur, Prabhsharan Kaur, Rohit Mittal. AI Application in the Creation of Medications for COPD. Research & Reviews: A Journal of Drug Design & Discovery. 2025; 12(02):01-05.
How to cite this URL:
Ramandeep Kaur, Prabhsharan Kaur, Rohit Mittal. AI Application in the Creation of Medications for COPD. Research & Reviews: A Journal of Drug Design & Discovery. 2025; 12(02):01-05. Available from: https://journals.stmjournals.com/rrjoddd/article=2025/view=214168


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Regular Issue Subscription Original Research
Volume 12
Issue 02
Received 25/04/2025
Accepted 06/06/2025
Published 23/06/2025
Publication Time 59 Days
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