Computational Molecular Biology 2024, Vol.14, No.1, 9-19 http://bioscipublisher.com/index.php/cmb 17 The definition of ethical responsibility is also an issue that cannot be ignored. When AI makes mistakes in drug design or causes adverse consequences, how to define liability becomes a difficult problem. Should AI developers, data providers or users bear the responsibility? This requires in-depth thinking and discussion, and the establishment of a complete responsibility definition mechanism to ensure that all parties can also assume corresponding ethical responsibilities while enjoying the convenience brought by AI technology. 4.3 Prospects for the innovation and ethical balance of artificial intelligence in drug design Facing the broad prospects of artificial intelligence in the field of drug design, we must realize that innovation and ethics do not exist in isolation, but are interdependent and mutually reinforcing. Future research should be committed to promoting technological innovation while ensuring that ethical principles are adhered to and respected. Technological innovation is the source of power for sustainable development in the pharmaceutical field, and the application of artificial intelligence brings new possibilities to drug research and development. Scientific researchers should be encouraged to continue to deeply explore the potential of AI in drug design, give full play to its unique advantages in data analysis, pattern recognition, etc., and provide more powerful tools and methods for the development of new drugs (Peña-Guerrero et al., 2021). However, technological innovation cannot be an excuse to ignore ethics. In the process of promoting AI applications, we must always adhere to the ethical bottom line and ensure that human dignity and rights are not violated. This requires always paying attention to ethical issues such as data privacy and security, decision-making transparency and explainability, and definition of responsibilities in research to ensure that technological innovation moves forward on a legal and compliant track. To balance the relationship between innovation and ethics, future research should focus on interdisciplinary collaboration and communication. Computer scientists, biomedical experts, jurists and ethicists should jointly participate in the research of AI and drug design, and jointly promote the healthy development of this field through knowledge integration and method innovation (Duch et al., 2019). This kind of interdisciplinary cooperation and exchange not only helps to improve the efficiency and quality of technological innovation, but also ensures that ethical principles are fully respected and reflected in technological innovation. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Brown N., Ertl P., Lewis R., Luksch T., Reker D., and Schneider N., 2020, Artificial intelligence in chemistry and drug design, Journal of Computer-Aided Molecular Design, 34: 709-715. https://doi.org/10.1007/s10822-020-00317-x Deng J., Yang Z., Ojima I., Samaras D., and Wang F., 2022, Artificial intelligence in drug discovery: applications and techniques, Briefings in Bioinformatics, 23(1): bbab430. https://doi.org/10.1093/bib/bbab430 Duch W., Swaminathan K., and Meller J., 2007, Artificial intelligence approaches for rational drug design and discovery, Current pharmaceutical design, 13(14): 1497-1508. https://doi.org/10.2174/138161207780765954 Gupta R., Srivastava D., Sahu M., Tiwari S., Ambasta RK, and Kumar P., 2021, Artificial intelligence to deep learning: machine approach intelligence for drug discovery, Molecular diversity, 25: 1315-1360. https://doi.org/10.1007/s11030-021-10217-3 Hessler G., Baringhaus KH, 2018, Artificial Intelligence in Drug Design, Molecules, 23(10): 2520. https://doi.org/10.3390/molecules23102520 Jiménez-Luna J., Grisoni F., Weskamp N., and Schneider G, 2021, Artificial intelligence in drug discovery: recent advances and future perspectives, Expert opinion on drug discovery, 16(9): 949 -959. https://doi.org/10.1080/17460441.2021.1909567
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