Bioscience Evidence 2024, Vol.14, No.1, 16-23 http://bioscipublisher.com/index.php/be 18 strict regulatory approval to ensure the safety and efficacy of the drugs (Qiu et al., 2020). This includes the design and implementation of clinical trials, as well as the evaluation of drug safety and efficacy. However, strict regulatory approval processes may lead to longer drug development cycles and increased research and development costs. In addition, regulatory agencies may have stricter and more cautious approval standards for rare disease drugs to ensure their safety and efficacy. In 2018, Kempf et al. delved into the specific challenges of conducting clinical trials for rare diseases, proposing to expand the patient base through international cooperation and multicenter trials, while utilizing patient registration systems and social media to improve recruitment efficiency. Through the challenge of developing rare disease drugs, the research team collaborates with international partners to conduct clinical trials. Recruiting patients in multiple countries simultaneously not only expands the patient base, but also accelerates the process of clinical trials (Kempf et al., 2018). Li et al. (2018) discovered CRISPR/Cas9 gene editing technology and its application in the field of biomedicine, including its important role in cancer treatment and precision medicine. The therapeutic effect of Keytruda (PD-1 inhibitor) on MSI-H/dMMR cancer was mentioned, as well as the innovative application of CRISPR technology in gene therapy, such as constructing more effective CAR-T cells through gene editing technology and identifying key genes in cancer immunotherapy. The 2024 Illness challenge foundation survey found that with the promotion of a series of policies in the field of rare disease treatment in China, the number of products developed and launched by Chinese companies, whether it is innovative drugs or generic drugs, is increasing. In 2023, Chinese pharmaceutical companies launched 8 new and generic drugs for rare diseases, except for 2 with dosage form/specification adjustments. It is foreseeable that with the continuous promotion of the rare disease field in China, more Chinese companies will expand into the rare disease field and further promote the listing of drugs in the rare disease field (https://img.frostchina.com/attachment/17091360/nfCpGPwYkbfzchSGz1saSN.pdf). 2 The Role of AI in Drug Discovery 2.1 Accelerate the identification of drug targets Artificial intelligence (AI) plays an important role in drug discovery, one of which is to accelerate the identification process of drug targets. Traditionally, the identification of drug targets is a complex and time-consuming process that requires extensive experimentation and data analysis (Tang et al., 2020). However, AI utilizes its powerful data processing and pattern recognition capabilities to efficiently analyze massive amounts of biological, genetic, and clinical data, identifying potential drug targets from them. Kubota et al. (2019) reviewed the application of chemical proteomics methods in target deconvolution in phenotypic drug discovery, discussing strategies such as affinity purification based on compound immobilized beads, photoaffinity labeling (PAL), cell thermal shift analysis (CETSA), and activity-based protein profiling (ABPP). Visibelli et al. (2023) found that during the treatment process, AAV serves as a carrier, carrying two zinc finger nucleases and a normal IDS gene directly to human liver cells. After reaching the interior of liver cells, zinc finger nuclease is specifically activated within liver cells to recognize, bind, and cleave endogenous albumin gene loci. By utilizing the innate DNA repair mechanism of cells, liver cells can insert genes encoding normal IDS into this site to complete individual cell repair (Figure 1). 2.2 Optimizing the compound screening process Another important role of artificial intelligence in drug discovery is to optimize the compound screening process. Traditional drug screening typically involves testing a large number of compounds in the laboratory to identify molecules that are active towards the target target (Chakravarty et al., 2021). However, this screening process is time-consuming and laborious, and cannot fully cover all possible compound spaces. AI can accelerate and optimize the process of compound screening through virtual screening and prediction models.
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