Computational Molecular Biology 2024, Vol.14, No.1, 20-27 http://bioscipublisher.com/index.php/cmb 24 2.2 Improve the accuracy and effectiveness of drugs The application of AI technology in drug discovery can also significantly improve the accuracy and effectiveness of drugs, and bring better treatment results to patients. LeCun (2015) showed that, on the one hand, AI can accurately predict the efficacy and side effects of drugs in different patient groups by analyzing a large number of patient data, providing strong support for individualized treatment. This concept of precision medicine is expected to help doctors develop more accurate treatment plans for patients, improve treatment effectiveness, and reduce unnecessary drug side effects. Mayr and Klambauer (2018) argue that AI technology can also predict the biological activity of drugs by simulating their interactions with biomolecules. This prediction is far more accurate than traditional methods and can significantly improve the success rate of drug development. At the same time, AI technology can also predict and evaluate the safety of drugs, reducing the risk of drug development. The application of artificial intelligence in the field of drug discovery has brought unprecedented opportunities for drug development. By accelerating the process of drug discovery and research and development, and improving the accuracy and effectiveness of drugs, AI technology is expected to bring revolutionary changes to the pharmaceutical industry and make greater contributions to human health. 2.3 Reduce R&D costs and risks Li et al. (2023) pointed out in their study that although drug research and development is a costly and risky activity, the application of AI technology can effectively reduce these costs and risks. Through its efficient screening and prediction capabilities, AI technology reduces the number of ineffective trial attempts and failed clinical trials, saving significant amounts of money for drug development. In addition, AI's role in real-time monitoring and analysis of experimental data helps researchers identify and solve problems in a timely manner and prevent losses caused by experimental failures. Koza et al. (2023) further explore how AI can mitigate R&D risks by deeply mining and analyzing existing data to open up new drug development ideas and strategies. In addition to the economic benefits, they also pointed out that the use of AI in the drug development process promotes cooperation and innovation across disciplines, infuses more inspiration and possibilities for drug development by integrating knowledge and technology from different fields, and promotes the continuous development and progress in the field of drug discovery. AI not only plays an important role in reducing the economics and risks of drug discovery, but also shows great potential in promoting scientific innovation and cross-field collaboration, providing new directions and strategies for future drug discovery. 3 The Challenge of AI in Drug Discovery 3.1 Data quality and privacy protection In the field of drug discovery, one of the challenges facing AI is inconsistent data quality and privacy issues. The quality of physical medicine data, chemical data and clinical data directly affects the training effect of AI models and the accuracy and reliability of the predicted results. Issues such as missing data, inaccurate labeling, and bias have become major obstacles to the application of AI in drug discovery. The study of Schneider et al. (2018) also emphasized that with the accumulation and application of a large amount of sensitive data, how to effectively protect data privacy has become an urgent issue. Patient data and clinical trial data involved in drug development often contain information about patient privacy, and the challenge is how to ensure data security while achieving effective sharing and utilization of data. Weber et al. (2019) also discuss the importance of data privacy protection in AI applications, especially when dealing with sensitive data involving personal health information. They explored the use of advanced encryption techniques and anonymization methods to address the balance between data sharing and privacy protection, emphasizing the importance of promoting data sharing while ensuring that individual privacy is not violated.
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