Computational Molecular Biology 2024, Vol.14, No.1, 20-27 http://bioscipublisher.com/index.php/cmb 23 Figure 3 Application of big data in drug design and discovery 2 Opportunities for AI in Drug Discovery 2.1 Accelerate the drug discovery and development process The traditional drug discovery process is a long and complex journey involving a large number of experiments and data analysis. Researchers need to select bioactive drug candidates from tens of thousands of compounds, and then conduct in-depth clinical trials to determine the safety and effectiveness of the drug. This process is not only time-consuming, but also costly. However, with the introduction of AI technology, the efficiency of drug discovery has been significantly improved. AI has strong deep learning and data mining capabilities, which can extract useful information from massive biomedical data. These data, including gene sequences, protein structures, disease pathogenesis, etc., are crucial for drug development. By deeply analyzing this data, AI can provide researchers with more precise research directions, reducing blindness and repetitive labor. Jimeno and Gaulton (2018) proposed that AI-based predictive models can quickly screen potential drug candidates by analyzing the structural characteristics and biological activity of compounds, greatly reducing experimental steps and improving research and development efficiency. At the same time, AI technology can also automate and optimize the steps of drug synthesis. Through intelligent algorithms, AI can predict and optimize the synthetic route of compounds, reducing the number and cost of experiments, and further shortening the cycle of drugs from development to market. In addition, the application of AI in drug discovery is also reflected in the field of personalized medicine. By deeply analyzing patients' genetic information and disease data, AI can tailor personalized treatment programs to patients, improving treatment outcomes and quality of life. This concept of precision medicine is gradually changing the traditional drug research and development model, bringing broader development prospects for the pharmaceutical industry.
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