Computational Molecular Biology 2024, Vol.14, No.1, 9-19 http://bioscipublisher.com/index.php/cmb 15 The 2023 research by Li Shuangxing and others focused on the application of artificial intelligence in the drug discovery process. Through in-depth analysis and empirical research, it revealed the key role of AI technology in accelerating the discovery and optimization of new drugs. This research uses a variety of AI algorithms and models to successfully shorten the drug development cycle and improve research and development efficiency. This important discovery has brought new breakthroughs to the pharmaceutical industry and provided new ideas and directions for future drug research and development (Li et al., 2023). Data is the fuel of AI, but in the field of drug design, high-quality data is not easy to obtain. Biomedical data are often scattered across different databases and laboratories, and suffer from standardization and annotation issues. In addition, data on rare diseases and emerging diseases are particularly scarce, which limits the training and application of AI models in these fields (Tripathi et al., 2022) . Although AI models perform well on certain tasks, their decision-making process in drug design is often a "black box." This lack of interpretability makes it difficult for researchers to trust model predictions, especially in drug development involving human health and life. At the same time, how to ensure that the decision-making of the AI model complies with ethical principles? How to supervise the drug development process based on AI technology to ensure its safety and effectiveness? These issues need to be faced and resolved jointly by scientific researchers, policymakers and regulatory agencies. As AI technology continues to develop, the relevant ethical and regulatory frameworks also need to be constantly updated and improved. However, these challenges have not prevented the application of AI in the field of drug design, but have created a large number of opportunities. London (2019) research believes that opaque decisions are more common in medicine than critics realize, and AI technology is changing all aspects of drug design, from target identification to candidate drug screening, optimization and clinical trial design. For example: Huawei Cloud Pangu Drug Molecular Large Model is a large model jointly developed by Huawei Cloud and Shanghai Institute of Materia Medica for the pharmaceutical field. This model can generate billions of new compound databases and improve the performance of multiple drug discovery tasks. It cooperated with the First Affiliated Hospital of Xi'an Jiaotong University to develop broad-spectrum antibacterial drugs, shortening the research and development cycle to one month and significantly improving efficiency. At the same time, the structure optimization function of the model can also help reduce the toxic and side effects of drugs on the human body. This achievement demonstrates the huge potential of artificial intelligence in drug research and development and is expected to bring revolutionary changes to the creation of new drugs. 3.3 Future development strategies for combining artificial intelligence with drug design In order to fully realize the potential of AI in drug design and cope with related challenges, a reasonable development strategy needs to be formulated. First, it is necessary to strengthen interdisciplinary cooperation and talent cultivation. Drug design involves knowledge and technology from multiple disciplines such as biology, chemistry, computer science, etc. Therefore, it is necessary to cultivate a group of compound talents who understand both drug design and AI technology. This will help promote the application and development of AI technology in drug design. Lee et al. (2022) found that the application of data preprocessing and big data and artificial intelligence methods enables accurate and comprehensive analysis of massive biomedical data, and Developing predictive models in the field of drug design will provide useful information in the era of biomedical big data. Establish a complete data sharing and intellectual property protection mechanism. Data is the core resource of AI technology, but the acquisition and sharing of data are often subject to many restrictions. Therefore, it is necessary to establish a reasonable data sharing mechanism to promote cooperation and exchanges among scientific researchers; at the same time, strengthen intellectual property protection and data privacy protection to ensure that the legitimate rights and interests of scientific researchers are protected (Noorbakhsh-Sabet et al., 2019). Continue to pay attention to and track the development of emerging technologies, and apply new technologies to the field of drug design in a timely manner. With the rapid development of science and technology, new AI technologies and algorithms continue to emerge, providing new ideas and methods for drug design. Thomas et al.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==