Computational Molecular Biology 2024, Vol.14, No.1, 9-19 http://bioscipublisher.com/index.php/cmb 16 (2022) believe that it is necessary to maintain sensitivity and foresight to new technologies, apply new technologies in practical work in a timely manner, and promote the in-depth integration and development of AI and drug design. The combination of artificial intelligence and drug design has broad prospects and huge potential. By strengthening cooperation, improving mechanisms and continuing innovation, we can expect more breakthroughs and results in this field in the future and make greater contributions to human health. 4 Conclusions and Suggestions 4.1 The importance and potential of artificial intelligence in the field of drug design The application of artificial intelligence in the field of drug design is gradually highlighting its importance and huge potential, bringing unprecedented opportunities to the pharmaceutical industry. Artificial intelligence has greatly improved the efficiency of drug research and development. The traditional drug research and development process is long and complex, but AI technology can quickly screen and optimize drug candidates through powerful computing power and advanced data analysis methods, significantly shortening the research and development cycle (Muller et al., 2022) . This not only reduces research and development costs, but also allows more potential therapeutic drugs to enter the clinical trial stage, providing patients with more treatment options. Artificial intelligence improves the precision of drug development. Selvaraj et al. (2022) found that AI can deeply mine information in biological data, accurately identify drug targets and biomarkers, and provide strong support for the formulation of precision medicine and personalized treatment plans. This means that future drugs will be more tailored to patients' individual differences, improve treatment effects, and reduce side effects. Of course, the potential of artificial intelligence in drug design is far from being fully exploited. With the continuous advancement and innovation of technology, AI is expected to play a greater role in the analysis of drug action mechanisms, the design of multi-target drugs, drug metabolism and toxicity prediction (Thomas et al., 2022). This will further promote innovation and development in drug research and development and bring more breakthroughs to human health. However, we must also be clearly aware that the application of artificial intelligence in the field of drug design still faces many challenges. Data quality, algorithm reliability, ethical issues, etc. all need to be carefully considered and resolved. Therefore, when promoting the application of AI technology in drug design, we should maintain a prudent and responsible attitude to ensure that technological innovation can truly benefit mankind. 4.2 Ethical issues that need to be considered when facing future technological development With the in-depth application of artificial intelligence in the field of drug design, a series of accompanying ethical issues have to be faced. These issues are not only related to the healthy development of technology, but also involve the moral bottom line, fairness and justice of human society. Data privacy and security issues have become urgent problems to be solved. During the drug development process, a large amount of patient data needs to be collected for training and optimization of AI models. This data often contains sensitive information such as patients' personal information and genomic data. Once leaked or misused, it will pose a great threat to patients' privacy and security. Therefore, a sound data protection mechanism must be established to ensure that patients' privacy rights are fully respected and protected (Paul et al., 2021). Transparency and explainability of AI decision-making have also received much attention. Brown et al. (2020) found that in drug design, AI models are often able to make complex decisions beyond human understanding. However, this "black box" decision-making process lacks transparency and explainability, making it difficult for people to understand and trust AI's decision-making results. This not only affects the widespread application of AI in drug design, but may also trigger public panic and resistance to AI technology. Therefore, it is necessary to continuously explore and improve the transparency and explainability of AI decision-making to enhance the public’s trust in AI technology.
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