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๐Ÿง‘๐Ÿผโ€๐Ÿ’ป Research - January 26, 2025

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

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โšก Quick Summary

This article explores the transformative role of Artificial Intelligence (AI) in drug discovery, highlighting its ability to streamline processes and reduce costs in the pharmaceutical industry. By leveraging vast amounts of open data, AI is paving the way for faster and more efficient drug development.

๐Ÿ” Key Details

  • ๐Ÿ“Š Focus: Role of AI in drug discovery
  • ๐Ÿงฉ Methods: Various AI models and techniques
  • โš™๏ธ Applications: Toxicity prediction, bioactivity assessment, and more
  • ๐Ÿ† Benefits: Enhanced productivity and reduced time in drug development

๐Ÿ”‘ Key Takeaways

  • ๐Ÿค– AI is revolutionizing traditional drug discovery methods.
  • ๐Ÿ’ก AI models can predict drug toxicity and bioactivity effectively.
  • ๐Ÿ” Applications include de novo drug discovery and interaction prediction.
  • ๐Ÿ“ˆ AI enhances the efficiency of clinical trials and drug repurposing.
  • ๐ŸŒ Open data is a crucial resource for AI-driven drug discovery.
  • ๐Ÿฅ AI’s role extends to predicting drug interactions and nanomedicines.
  • ๐Ÿ“… Recent advancements in AI have made it a vital tool in pharmaceuticals.
  • ๐Ÿงช Collaboration between AI and traditional methods is essential for success.

๐Ÿ“š Background

The traditional methods of drug discovery, including wet-lab testing and synthetic techniques, are often time-consuming and costly. As the pharmaceutical industry faces increasing pressure to innovate and reduce costs, the integration of AI technologies offers a promising solution to enhance the drug development process.

๐Ÿ—’๏ธ Study

This review article discusses the various applications of AI in the pharmaceutical sector, focusing on its ability to predict a drug’s toxicity, bioactivity, and physicochemical properties. The authors highlight the development and implementation of numerous AI-based models that serve as supplements to conventional research methodologies.

๐Ÿ“ˆ Results

The findings indicate that AI can significantly improve the drug discovery process by providing insights into target structure prediction, interaction prediction, and binding affinity prediction. These advancements lead to faster identification of potential drug candidates and more efficient clinical trials.

๐ŸŒ Impact and Implications

The integration of AI in drug discovery has the potential to revolutionize the pharmaceutical industry. By enhancing the speed and accuracy of drug development, AI can lead to the discovery of new therapies and improve patient outcomes. This shift not only benefits pharmaceutical companies but also has far-reaching implications for global health.

๐Ÿ”ฎ Conclusion

The role of AI in drug discovery is becoming increasingly vital as the pharmaceutical industry seeks to innovate and improve efficiency. This article underscores the transformative potential of AI in streamlining drug development processes and enhancing productivity. Continued research and collaboration between AI technologies and traditional methods will be essential for future advancements in this field.

๐Ÿ’ฌ Your comments

What are your thoughts on the integration of AI in drug discovery? We would love to hear your insights! ๐Ÿ’ฌ Share your comments below or connect with us on social media:

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

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 de novo drug discovery, target structure prediction, interaction prediction, and binding affinity prediction. AI for predicting drug interactions and nanomedicines were also considered.

Author: [‘Singh PK’, ‘Sachan K’, ‘Khandelwal V’, ‘Singh S’, ‘Singh S’]

Journal: Recent Pat Biotechnol

Citation: Singh PK, et al. Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications. Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications. 2025; 19:35-52. doi: 10.2174/0118722083297406240313090140

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