Computational Molecular Biology 2024, Vol.14, No.1, 9-19 http://bioscipublisher.com/index.php/cmb 10 Through this study, we hope to further deepen our understanding of the role of AI in drug design and grasp its future development trends and potential challenges. At the same time, we also hope to trigger more in-depth thinking on how to balance technological innovation and ethical responsibility, and explore a path that not only promotes the healthy development of the field of drug design, but also respects ethics and morals. This not only has far-reaching significance in promoting progress in the field of drug design, but also provides useful reference and inspiration for the application of AI technology in wider fields. We firmly believe that under the dual guidance of technology and ethics, AI will create more miracles in the field of drug design and make greater contributions to human health. 1 Application of Artificial Intelligence Technology in Drug Design 1.1 Types of artificial intelligence technologies and their applicability in drug design Artificial intelligence technology covers multiple branches such as machine learning, deep learning, natural language processing, and reinforcement learning. Each technology has its unique application and applicability in drug design. For example, machine learning algorithms can be used to model the chemical structure and biological activity of known drug molecules, thereby predicting the potential activity of new molecules and guiding the design and optimization of drug molecules. In addition, machine learning can also be used to predict drug side effects, helping researchers avoid potential safety risks during the design stage. Wang et al. (2019) found that as drug design enters the era of big data, ML methods have gradually evolved into a deep learning (DL) method with stronger generalization capabilities and more effective big data processing, which further promotes the combination of artificial intelligence technology and computer-aided drug design technology promotes the discovery and design of new drugs. Zhong et al. (2018) found that deep learning technology can process more complex drug molecular structure information, such as three-dimensional conformation, intermolecular interactions, etc. By building a deep neural network model, deep learning can more accurately predict the interaction between drug molecules and targets, providing more accurate guidance for drug design. Thomas et al. (2022) discovered the antiviral drug Paxlovid designed for 3CL protease and the anti-tumor drug developed for KRAS protein. The success of these new drug discoveries all starts with the selection of targets and benefits from the assistance of AI technology. Natural language processing technology can assist scientific researchers in extracting useful information from massive documents and patents, such as the efficacy, side effects, and mechanisms of action of known drugs. Reinforcement learning is an artificial intelligence technology that learns interactively between an agent and the environment. By constructing a virtual environment that simulates the interaction between drug molecules and organisms, reinforcement learning algorithms can automatically explore and optimize the structure of drug molecules to maximize their effectiveness. efficacy and minimizing its side effects. Different artificial intelligence techniques have different applicability in drug design. Machine learning is suitable for modeling and predicting large amounts of data; deep learning is suitable for processing complex drug molecule structure information and making accurate predictions; reinforcement learning is suitable for optimizing the design process of drug molecules. 1.2 Application of artificial intelligence technology in various stages of drug design Artificial intelligence technology plays a vital role in all stages of drug design, bringing revolutionary changes to drug research and development. In the target identification stage, artificial intelligence technology helps researchers quickly and accurately identify potential drug targets related to specific diseases by analyzing big data such as genomics and proteomics. Hessle and Baringhaus (2018) found that deep neural networks showed improved predictability compared to baseline machine learning methods. At the same time, the scope of AI applications in early-stage drug discovery has expanded widely, such as de novo design of compounds and peptides and synthesis planning.
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