Computational Molecular Biology 2024, Vol.14, No.1, 9-19 http://bioscipublisher.com/index.php/cmb 13 and compliance; on the other hand, it is also necessary to strengthen the supervision of the data sharing process to prevent data from being tampered with, abused or used for other illegal purposes (Zhang et al., 2022). With the continuous development of artificial intelligence technology, the value and importance of data have become increasingly prominent. This makes data a target for various attacks and theft. Therefore, it is necessary to continuously improve data security protection capabilities and adopt the latest technical means and methods to deal with various network attacks and data leakage incidents. 2.2 Transparency and explainability of artificial intelligence decision-making In the field of drug design, the transparency and explainability of artificial intelligence decision-making have become the focus of public attention, scientific researchers and regulatory agencies. This is not only because of the advancement of technology, but also because AI decision-making is directly related to human health and life safety. On August 15, 2021, Professor Liang Zheng, Vice Dean of the Institute of International Governance of Artificial Intelligence at Tsinghua University, attended "The 4th Issue of the Future Forum AI Ethics and Governance Series - Reliability and Explainability of AI Decision-Making". Professor Liang Zheng pointed out that reliable AI should have four major elements: security, fairness, transparency, and privacy protection. Therefore, "trustworthiness" and "explainability" are positively related, especially for users and the public. Implementing algorithm explainability is an important part of ensuring reliability and trust (https://aiig.tsinghua.edu.cn/ info/1296/1328.htm). Transparency, simply put, refers to the extent to which the processes and logic behind AI decisions can be understood and viewed. In drug design, an AI model might recommend a certain molecular structure as a potential drug candidate based on millions of data points and complex algorithms. But the question is, how are these decisions made? What data is it based on? What algorithms are used? Deng et al. (2022) studied common data resources, molecular representations, and benchmark platforms to decompose artificial intelligence technology into model architectures and learning paradigms. Reflects the technical development of artificial intelligence in drug discovery over the years and provides a GitHub repository containing a series of papers (and code, if applicable) as a learning resource, which is updated regularly. These need to be made clear. Transparency requires that the AI system can provide sufficient information to allow external observers to understand the basis and logic of its decisions. Explainability goes a step further, requiring AI to not only demonstrate its decision-making process, but also explain the reasons for its decisions in a way that humans can understand. In drug design, this means that AI needs to be able to explain why a certain molecular structure was chosen and not others. This explanation cannot be just "because the algorithm says so", but should be based on specific chemical or biological principles or known experimental results. However, achieving transparency and explainability is not easy. The decision-making process of AI often involves large amounts of data and complex calculations, which is difficult to describe in simple language. In addition, some AI models themselves are "black box" models, and their internal logic is not easy to understand. Therefore, researchers need to continuously explore new methods and technologies to improve the transparency and explainability of AI decision-making (Schneider et al., 2020). 2.3 Definition of ethical responsibilities of artificial intelligence in drug design In the field of drug design, the application of artificial intelligence is becoming more and more widespread. However, it is followed by a series of complex ethical issues, the most core of which is the definition of ethical responsibility. The ethical responsibility of artificial intelligence in drug design is not a simple "yes or no", but a complex issue involving multiple levels and requiring careful consideration. It must be recognized that although artificial intelligence has powerful computing power and data analysis capabilities, it is still a tool based on human programming and algorithms. Therefore, Jing et al. (2018) found that
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