Computational Molecular Biology 2024, Vol.14, No.1, 9-19 http://bioscipublisher.com/index.php/cmb 14 in the drug design process, when artificial intelligence makes a certain decision or recommendation, the responsibility behind it should actually be traced back to the humans who designed, programmed, and used these AI tools. This means that the blame cannot simply be placed solely on AI itself, but rather the role and responsibilities of humans within it should be explored more deeply. Artificial intelligence decisions and recommendations in drug design are often based on large amounts of data and complex algorithms. When these decisions or recommendations lead to undesirable consequences, how to define responsibility becomes a difficult problem. In this case, an in-depth review and analysis of the AI’s decision-making process is required to determine whether there are issues such as design flaws, data bias, or algorithm errors. At the same time, it is also necessary to conduct a comprehensive investigation and assessment of relevant responsible parties, such as AI developers, data providers, users, etc., to determine their respective responsibilities and obligations (Jiménez-Luna et al., 2021). In order to better define the ethical responsibilities of artificial intelligence in drug design, complete laws, regulations and regulatory mechanisms need to be established. These laws and regulations should clarify the status and role of AI in drug design, as well as the responsibilities and obligations of all relevant parties. At the same time, the regulatory mechanism should conduct comprehensive supervision and evaluation of the decision-making process of AI to ensure its legality and compliance. 3 Future Prospects of Artificial Intelligence and Drug Design 3.1 Innovation and development trends of artificial intelligence technology Artificial intelligence technology has made breakthrough progress in recent years, especially in the fields of deep learning, reinforcement learning, natural language processing and other fields. The development of these technologies provides powerful tools and methods for drug design. In the future, artificial intelligence technology will continue to develop in a more efficient, more accurate, and more intelligent direction. The continuous optimization and innovation of AI algorithms will bring more efficient and accurate computing capabilities to drug design. For example, algorithms such as deep learning and reinforcement learning have shown great potential in drug molecular structure prediction and activity screening. As Staszak et al. (2022) pointed out, neural networks have great potential in generating generalization based on relatively narrow training data. The potential is that deep learning can combine seemingly distant phenomena and influences and connect facts in a way similar to human thinking. Natural language processing is suitable for extracting useful information from literature. These algorithms can automatically learn and extract information from data. Characteristics, discover new drug action mechanisms and targets, thereby accelerating the drug development process. With the continuous development of big data technology, AI's data processing and analysis capabilities in drug design will also be greatly improved. In the future, scientific researchers can use AI technology to conduct in-depth mining and analysis of massive biomedical data, discover new drug action mechanisms and biomarkers, and provide stronger support for drug research and development (Zhang et al., 2022). AI technology will also play an important role in the clinical trial phase of drug design. Gupta et al. (2022) study demonstrated that artificial intelligence principles have been applied to classification of active and inactive, drug release monitoring, preclinical and clinical development, primary and secondary drug screening, biomarker development, drug manufacturing, biological activity Identification and physicochemical properties, toxicity prediction, and mode of action identification. 3.2 Challenges and opportunities faced by the field of drug design The field of drug design stands at a transformative moment driven by artificial intelligence technology. With the continuous advancement of algorithms and the explosive growth of data volume, AI has brought unprecedented speed and accuracy to drug research and development. However, this convergence also brings with it a set of challenges that, along with opportunities, are shaping the future of drug design.
RkJQdWJsaXNoZXIy MjQ4ODYzNA==