Computational Molecular Biology 2024, Vol.14, No.1, 20-27 http://bioscipublisher.com/index.php/cmb 25 3.2 Interpretability and transparency In drug discovery, model interpretability and transparency pose one of the key challenges. Miller et al. (2019) note that the AI models on which drug discovery relies are often built based on deep learning and neural network techniques. The models created by these technologies often exist as "black boxes" whose inner workings and decision-making processes are difficult to understand intuitively, and this lack of interpretability and transparency can undermine trust and acceptance of the models' predictions. In their study, Wilson and Martinez (2020) discuss the importance of increasing the interpretability and transparency of AI models in the field of drug discovery. In the drug discovery process, a clear understanding of the model's predictions and recommendations is essential, as it is the basis for subsequent validation and experimental activities. Meanwhile, Wilson and Martinez (2020) explore various approaches and techniques to enhance the interpretability and transparency of AI models in drug discovery applications. Together, they reveal the challenges of interpretability and transparency of AI models in drug discovery and highlight the need to enhance these attributes. This involves not only technical improvements, but also in-depth understanding and interpretation of the model's decision logic, thus enhancing the application value and practicality of the model in drug discovery. Addressing this challenge is critical to building trust in model predictions and facilitating advances in drug discovery. 3.3 Ethical and legal issues With the widespread use of artificial intelligence (AI) in drug discovery, ethical and legal issues are emerging. Drug development is a complex and sensitive process that involves large amounts of personal information and clinical trial data. The legitimate access and use of these data is directly related to the rights of patients and the credibility of research. Therefore, it is essential to comply with relevant privacy protection laws and regulations. The study by Hessler et al. (2022) delves into this issue and highlights the importance of compliance with privacy protection laws and regulations. They pointed out that in the process of drug development, the personal privacy information involved includes patients' identity information, health status, genetic information, etc., and the disclosure of these information may bring serious consequences to patients, such as identity theft and discrimination. Ensuring the legitimacy of data is not only a legal requirement, but also the key to maintaining patient trust and willingness to participate in research. In practice, drug discovery organizations need to take a number of measures to ensure the legitimacy and security of data, and must comply with relevant privacy laws and regulations, such as the European Union's General Data Protection Regulation (GDPR) and the U.S. Health Insurance Mobility and Accountability Act (HIPAA). These laws have made clear provisions on the collection, storage, use and sharing of data, providing legal protection for drug research and development institutions. In the study of Sun et al. (2021), the intellectual property and patent issues that may be encountered when artificial intelligence is applied in drug discovery are discussed. Because drug development involves a large number of technological innovations and patent applications, how to protect the intellectual property of research and development results becomes a challenge, highlighting the need to develop a clear IP protection strategy and rational use of the patent system. The drug development process also requires strict adherence to ethical codes and ethical standards to ensure legal compliance with research. This includes, but is not limited to, ensuring informed patient consent, safeguarding the well-being of research subjects, and reasonably sharing research results. Solutions to the ethical and legal challenges that must be faced when using AI for drug discovery are critical to ensuring the legitimacy of scientific research, protecting patient rights, and promoting scientific and technological innovation. 4 Future and Outlook The application of artificial intelligence in drug discovery has made remarkable progress. Through molecular design and drug screening, optimization of the drug development process, and the application of AI in clinical trial design and data analysis, AI technology has become an important tool for accelerating drug discovery and development. Many research institutions and biotechnology companies are actively exploring the application of
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