Bioscience Method 2024, Vol.15, No.1, 37-49 http://bioscipublisher.com/index.php/bm 46 R&D process: The researchers first used AlphaFold to predict the three-dimensional structure of the main protease (Mpro) of COVID-19. Subsequently, they utilized this structural information to screen candidate drugs that may bind to Mpro from known drug databases. After further experimental verification, they successfully found several candidate drugs with the activity of inhibiting COVID-19. Case achievement: Based on the AI drug screening method of AlphaFold, scientific researchers screened a variety of potential candidate drugs in a short time, providing strong support for drug research and development of COVID-19 (Jamilloux et al., 2020). These methods not only shorten the drug development cycle, but also reduce research and development costs, making important contributions to the global fight against the epidemic. 5.2 Key factors and application value for the success of the above cases The key factors for the success of the above cases mainly include advanced AI technology, large-scale computing power, interdisciplinary cooperation, and rapid data acquisition and integration. AlphaFold, as an advanced deep learning tool, can accurately predict the three-dimensional structure of proteins, providing an important foundation for drug screening (Jamilloux et al., 2020). In addition, large-scale computing power ensures the efficient operation of AI models, while interdisciplinary collaboration promotes close collaboration among researchers in different fields. The rapid data acquisition and integration capabilities enable researchers to quickly respond to global health crises such as the pandemic, providing strong support for drug research and development. The application value of this case is mainly reflected in the following aspects. Firstly, AI technology can greatly accelerate the drug development process, improve research and development efficiency, and shorten the time to market for new drugs. Secondly, by reducing the need for experimental verification, AI technology can help reduce the cost of drug development. In addition, by predicting and screening a large number of compounds, AI technology can improve the success rate of drug development and screen out more potential candidate drugs. Finally, in response to the global health crisis, AI technology has provided researchers with fast and efficient drug development methods, making important contributions to the global fight against crises such as the pandemic. 6 Discussion and Outlook The current AI based drug screening process shows obvious advantages and disadvantages. The advantage lies in its speed and efficiency. AI technology can quickly process and analyze large amounts of data, significantly reducing drug screening time and improving research and development efficiency. The accuracy of AI algorithms is also higher, which can more accurately predict the interaction between drugs and targets, reducing the need for experimental verification (Neves et al., 2018). AI drug screening also helps to reduce the overall cost of drug development, achieving economic benefits by reducing the number of experiments and labor costs. However, its shortcomings cannot be ignored. The accuracy of AI models highly depends on the quality and quantity of input data. If there is bias or inadequacy in the data, it may lead to misleading screening results. At the same time, the interpretability of current AI models is insufficient, making it difficult for researchers to understand the working principle of the models and the basis for screening results. In addition, AI drug screening may also involve complex issues such as data privacy, intellectual property, and ethical review, which need to be handled with caution. At present, technological challenges and data challenges are the two main challenges. It is necessary to improve the accuracy and reliability of AI models, especially when dealing with complex and diverse biological data. The data challenge lies in obtaining high-quality and diverse biological data, and solving the problems of data annotation and integration. Possible solutions to these challenges include continuous research and optimization of AI models, strengthening interdisciplinary cooperation to jointly promote the application of AI in drug development, and establishing a strict data governance system to ensure data quality and accuracy. The AI based drug screening process is expected to present more development trends and potential impacts. Model integration and fusion may become an important direction in the future, improving the accuracy and efficiency of drug screening by integrating and fusing different types of AI models. The development of personalized healthcare will also benefit from the promotion of AI drug screening, which selects the most suitable
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