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Your search keyword '"Zhongjian Xie"' showing total 3 results
Start Over "Zhongjian Xie" Topic drug discovery Publication Year Range This year
3 results on '"Zhongjian Xie"'

1. The recent advances in the approach of artificial intelligence (AI) towards drug discovery

Author

Mahroza Kanwal Khan, Mohsin Raza, Muhammad Shahbaz, Iftikhar Hussain, Muhammad Farooq Khan, Zhongjian Xie, Syed Shoaib Ahmad Shah, Ayesha Khan Tareen, Zoobia Bashir, and Karim Khan

Subjects
AI, drug discovery, machine learning, structure-activity relationship, artificial intelligence, Chemistry, QD1-999
Abstract

Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.

Published
2024
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2. The recent advances in the approach of artificial intelligence (AI) towards drug discovery.

Author

Khan, Mahroza Kanwal, Raza, Mohsin, Shahbaz, Muhammad, Hussain, Iftikhar, Khan, Muhammad Farooq, Xie, Zhongjian, Shah, Syed Shoaib Ahmad, Tareen, Ayesha Khan, Bashir, Zoobia, Khan, Karim, and Sagar, Rizwan Ur Rehman

Subjects
ARTIFICIAL intelligence, DRUG discovery, DRUG design, STRUCTURE-activity relationships, MACHINE learning
Abstract

Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery. [ABSTRACT FROM AUTHOR]

Published
2024
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3. New Artificial Intelligence Study Results Reported from Shenzhen University [The recent advances in the approach of artificial intelligence (AI) towards drug discovery].

Subjects
DRUG discovery, ARTIFICIAL intelligence, TECHNOLOGICAL innovations, MACHINE learning, ENVIRONMENTAL chemistry
Abstract

A recent study from Shenzhen University in China explores the role of artificial intelligence (AI) in drug discovery. The researchers highlight the potential of AI to revolutionize drug discovery, but also acknowledge its limitations and ethical concerns. The study provides an overview of AI-based drug discovery techniques, such as virtual screening and de novo drug design, and discusses the gaps in traditional drug design methods. The researchers also propose strategies to overcome current challenges and emphasize the importance of understanding the potential of AI in drug discovery. [Extracted from the article]

Published
2024

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