Computational Molecular Biology 2024, Vol.14, No.1, 9-19 http://bioscipublisher.com/index.php/cmb 12 1.3 Analysis of the impact of artificial intelligence technology on drug design efficiency and success rate Artificial intelligence technology has had a profound impact on the efficiency and success rate of drug design, and has greatly promoted progress in the field of drug research and development. At all stages of drug design, artificial intelligence technology has significantly improved work efficiency. The traditional drug design process requires a lot of manual experiments and data analysis, which is time-consuming and labor-intensive. Artificial intelligence technology can quickly process and analyze large-scale data sets through automated and intelligent methods, thus greatly shortening the time cycle of drug design (Moingeon et al., 2022). For example, Exscientia cooperates with Japan's Sumitomo Dainippon Pharma to use artificial intelligence platforms to automatically generate and screen drug molecules, accelerating the drug discovery process. Public data shows that this technology shortens drug development time from 5-10 years to 1-2 years, improving the success rate. During the cooperation, a number of innovative drug candidates in cancer, neurological diseases and other fields have been discovered and entered the clinical trial stage (https://zhuanlan.zhihu.com/p/114953741). Artificial intelligence technology has also significantly improved the success rate of drug design. In the traditional drug design process, there is often a high failure rate due to limitations in experimental conditions, data quality, analysis methods and other factors. Artificial intelligence technology can screen potential drug candidates at an early stage through accurate data analysis and prediction models, thereby reducing the risk of later experimental failure. Zhang Minquan et al. (2024) found that artificial intelligence technology uses big data to screen out corresponding compounds for molecular simulation, and feeds the simulation results back to the artificial intelligence system for learning, and continuously optimizes the artificial neural network. The combined use of artificial intelligence and molecular simulation technology improves the efficiency of drug design research, reduces the impact of human factors on simulation results, and increases the credibility of simulation results. For example, in the preclinical research stage, artificial intelligence can use machine learning algorithms to accurately predict the biological activity, pharmacokinetic properties, and toxicity of candidate drugs, helping researchers discover potential problems in advance and optimize them, thus improving the quality of drugs. Success rate in entering clinical trials. In addition, artificial intelligence can also discover biomarkers and risk factors closely related to patient efficacy and safety by mining and analyzing clinical trial data, providing strong support for the design and optimization of clinical trials, and further improving the success rate of drug development. 2 Ethical Considerations in Artificial Intelligence and Drug Design 2.1 Data privacy and security issues In the process of applying artificial intelligence to drug design, data privacy and security issues are particularly critical. This involves how to reasonably and legally collect, store and use large amounts of biometric data, medical information, patient records and other sensitive content. The protection of data privacy is a dual ethical and legal requirement. Murdoch (2021) research stated that patients' personal information, genetic data, etc. are highly sensitive information, and once leaked, it may have a serious impact on the patient's life, work, and even personal safety. Therefore, when collecting these data, the patient’s explicit consent must be obtained and their rights to information, choice, and refusal must be fully respected. At the same time, the data storage and transmission process also requires strict encryption to prevent data from being illegally obtained or abused. Pesapane et al. (2018) analyzed the legal framework regulating medical devices and data protection in Europe and the United States, assessed the developments currently taking place, and stated that data security issues cannot be ignored. During the drug design process, large amounts of data need to be shared and exchanged between different institutions, platforms and even countries. This brings great challenges to data security. On the one hand, it is necessary to establish a complete data sharing mechanism to ensure that data flows under the premise of legality
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