Bioscience Method 2024, Vol.15, No.1, 37-49 http://bioscipublisher.com/index.php/bm 37 Review and Progress Open Access AI Based Drug Screening Process: From Data Mining to Candidate Drug Validation Wangwei Institute of Life Science,Jiyang College of Zhejiang A&F University, zhuji, 311800, China Corresponding email: 2741098603@qq.com Bioscience Method, 2024, Vol.15, No.1 doi: 10.5376/bm.2024.15.0005 Received: 11 Jan., 2024 Accepted: 16 Feb., 2024 Published: 28 Feb., 2024 Copyright © 2024 Wang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang W., 2024, AI based drug screening process: from data mining to candidate drug validation, Bioscience Method, 14(1): 37-49 (doi: 10.5376/bm.2024.15.0005) Abstract With the rapid development of artificial intelligence (AI) technology, its application in drug research and development is becoming increasingly widespread. This study introduces the advantages of AI technology in drug screening, such as fast processing and analysis of large amounts of data, improving screening accuracy, and reducing research and development costs. Discussed the shortcomings in the current AI drug screening process, such as data dependence, insufficient model interpretability, and legal and ethical issues. Intended to explore the AI based drug screening process, from data mining to candidate drug validation. I hope to provide a comprehensive and systematic perspective for researchers and practitioners in the field of drug development by deeply understanding the advantages, disadvantages, and challenges faced by AI technology in drug screening, and proposing corresponding solutions, in order to guide them to better utilize AI technology to accelerate the drug development process. Keywords Artificial intelligence; Drug screening; Data mining; Candidate drug validation; Drug development Drug development is a crucial link in the pharmaceutical field, with immeasurable value in improving human health and treating diseases (Wu et al., 2019). The discovery and development of new drugs can provide new treatment plans for various diseases, improve the treatment effect of diseases, reduce treatment costs, and even find breakthroughs for some currently incurable diseases. Drug development can not only improve the quality of life of individual patients, but also have a positive impact on the overall health level of society. Ping has a positive impact. However, the traditional drug development process is time-consuming and labor-intensive, and the success rate is often not satisfactory. This is mainly attributed to the complexity of biological systems, the complexity of drug target interactions, and the vast space for candidate drugs. With the rapid development of biotechnology, the standards and requirements for drug research and development are also constantly improving, which brings greater challenges to drug research and development. Therefore, developing more efficient and accurate drug screening methods has become an urgent task. In recent years, the rapid development of artificial intelligence (AI) technology has brought revolutionary changes to the field of drug research and development (Mak and Pichika, 2019). AI can use technologies such as deep learning and machine learning to extract valuable information from massive data, predict drug target interactions, evaluate drug efficacy and safety. This not only greatly improves the efficiency and accuracy of drug screening, but also provides new ideas and directions for drug development. The rise of AI technology, especially the development of data mining and machine learning technologies, has provided new possibilities and enormous potential for drug screening. Data mining technology can efficiently process and analyze a large amount of biomedical data, extract information related to drug activity, safety, etc. Machine learning algorithms can utilize this data to construct accurate prediction models, helping researchers quickly screen potential candidate drugs. This study aims to explore how to combine data mining and machine learning techniques to construct an efficient AI drug screening process. Through in-depth analysis and discussion of the advantages, challenges, and future
RkJQdWJsaXNoZXIy MjQ4ODYzMg==