Bioscience Evidence 2024, Vol.14, No.1, 16-23 http://bioscipublisher.com/index.php/be 19 Figure 1 In vivo genome editing of albumin: harnessing the liver's mosthighly expressed locus (Li et al., 2018) Li et al. (2018) discussed the application of AI in identifying potential drugs and how it can help reduce the failure rate of drug development, potentially improving some of the biggest challenges faced by the pharmaceutical industry, such as high failure rates in clinical trials. Emphasis was placed on the collaboration between the AI based technology platform Atomwise and over 20 research institutions and pharmaceutical companies, showcasing the broad application prospects of AI technology in the pharmaceutical field. Lim et al. (2016) proposed a fast and accurate method for predicting drug targets, which can explore chemical and protein spaces and their interactions on a large scale, helping to reposition drugs and predict drug side effects. 2.3 Predicting drug efficacy and side effects Another important role of artificial intelligence in the discovery of rare disease drugs is to predict the effectiveness and side effects of drugs. In the process of drug development, understanding the efficacy and potential side effects of drugs is crucial, and AI can provide accurate and fast predictive models to help researchers better evaluate the efficacy and safety of drugs. Through machine learning and deep learning algorithms, AI can analyze a large amount of biological data, drug chemical structures, and clinical trial results to establish predictive models for predicting drug efficacy (Visibelli et al., 2023). These models can consider multiple factors, such as the mechanism of action of drugs, molecular interactions, biological pathways, etc., in order to predict the therapeutic effect of drugs on specific diseases. Walters and Barzilay (2021) reviewed the application of AI in drug discovery, including property prediction, molecular generation, image analysis, and organic synthesis planning, and evaluated the potential and challenges of AI technology in improving drug discovery efficiency. It can help researchers screen and optimize candidates in the early stages of drug development, thereby selecting the most promising drug candidates more targeted. 3 Case Study on AI Driving the Discovery of Rare Disease Drugs 3.1 Success case analysis In the field of AI driving the discovery of rare disease drugs, some remarkable successful cases have emerged. For example, in 2016, an application called Breathe RM, led by Andres Floto, an outstanding professor of respiratory biology at the University of Cambridge, was changing the way patients with cystic fibrosis (CF) are cared for (Viviani et al., 2016). This innovative technology, through advanced algorithms, can predict in advance when patients will fall ill, up to 10 days in advance. This breakthrough technology will redefine the monitoring methods for CF patients, enabling remote tracking and reducing frequent and time-consuming visits. The concept of Breathe RM is a comprehensive to-do list that serves as a key tool for remote monitoring of CF patients. By simplifying the collection and prediction of important information required for potential health complications, this application requires users to input daily data. The application seamlessly integrates with handheld spirometers for measuring lung function and nail oximeters for assessing blood oxygen levels. Through
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