In an era of rapid technological advancement, artificial intelligence (AI) has undoubtedly emerged as one of the most innovative and promising technological forces in the world. The research by Mak et al. (2019) profoundly revealed the disruptive application of AI in many fields such as image recognition, natural language processing, and data analysis. It not only greatly improved production efficiency, but also gave birth to profound changes in many industries. Today, the wave of AI has swept across like a sudden storm to drug design and discovery, a holy land involving professional fields such as gene sequence analysis, molecular docking, and drug effect prediction. With its excellent computing power, the wisdom of deep learning and the accuracy of pattern recognition, AI provides a new perspective and tools for drug design. It uses machine learning algorithms to efficiently analyze massive biological data, and uses natural language processing technology to parse medical literature and research results, providing unprecedented possibilities for drug discovery (Srivastav a et al., 2023).

Looking back at the initial involvement of AI in the field of drug design, it mainly focused on auxiliary calculations and simulation experiments. However, with the rapid advancement of cutting-edge technologies such as deep learning and machine learning, AI has been deeply involved in every aspect of drug design, from target identification to molecular screening to the optimization of clinical trials. Its presence can be seen everywhere. This transformation not only significantly shortens the drug development cycle and reduces research and development costs, but more importantly, AI, with its unique insights and computing power, assists scientists in discovering drug candidates that are difficult to reach with traditional methods, and provides a basis for overcoming complex diseases. Provides new opportunities and hopes (Singh et al., 2023).

However, just as all technological progress is accompanied by ethical challenges, the application of AI in the field of drug design also faces many ethical dilemmas. Issues such as data privacy, algorithm transparency, and responsibility attribution have gradually surfaced, triggering widespread concern and in-depth discussions from all walks of life. Therefore, this study aims to comprehensively examine the current application status of AI in the field of drug design, look forward to future development trends, and deeply explore the accompanying ethical considerations, with a view to providing valuable reference and guidance for scholars and practitioners in related fields.

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.

In the molecular screening and optimization stage, artificial intelligence technology helps researchers quickly select potentially active candidate molecules from a huge compound library by building prediction models, and optimizes the structure of these molecules to improve their efficacy and reduce side effects. For example, the virtual screening method based on machine learning (Figure 1) can use the chemical structure and biological activity data of known active molecules to build a prediction model to predict and rank the activity of new molecules, thereby quickly screening out candidate molecules with potential activity.

Artificial intelligence technology also plays an important role in the clinical trial stage. For example, DeepMind collaborated with Moorfields Eye Hospital to develop the Streams system, which uses deep learning technology to analyze eye scan images, automatically identify and interpret complex images, and provide preliminary diagnostic recommendations (https://zhuanlan.zhihu.com/p/41970785). This technology helps solve the problem of scarce expert resources and allows patients to receive timely diagnosis. In addition, prediction models based on machine learning can also predict and evaluate the results of clinical trials, providing strong support for the design and optimization of clinical trials. These applications can not only improve the efficiency and accuracy of clinical trials, but also help reduce the costs and risks of clinical trials.

Zhang et al. (2022) found that despite a large investment of money and time, the success rate of clinical testing is still less than 15%. Approximately 50% of drug discovery failures are due to poor pharmacokinetic properties (absorption, distribution, metabolism, excretion and toxicity). With the development of computational methods, the speed and success rate of drug discovery have greatly improved.

1.3 Analysis of the impact of artificial intelligence technology on drug design efficiency and success rate

Artificial intelligence technology has had a profound impact on the efficiency and success rate of drug design, and has greatly promoted progress in the field of drug research and development.

At all stages of drug design, artificial intelligence technology has significantly improved work efficiency. The traditional drug design process requires a lot of manual experiments and data analysis, which is time-consuming and labor-intensive. Artificial intelligence technology can quickly process and analyze large-scale data sets through automated and intelligent methods, thus greatly shortening the time cycle of drug design (Moingeon et al., 2022). For example, Exscientia cooperates with Japan's Sumitomo Dainippon Pharma to use artificial intelligence platforms to automatically generate and screen drug molecules, accelerating the drug discovery process. Public data shows that this technology shortens drug development time from 5-10 years to 1-2 years, improving the success rate. During the cooperation, a number of innovative drug candidates in cancer, neurological diseases and other fields have been discovered and entered the clinical trial stage (https://zhuanlan.zhihu.com/p/114953741).

Artificial intelligence technology has also significantly improved the success rate of drug design. In the traditional drug design process, there is often a high failure rate due to limitations in experimental conditions, data quality, analysis methods and other factors. Artificial intelligence technology can screen potential drug candidates at an early stage through accurate data analysis and prediction models, thereby reducing the risk of later experimental failure.

Zhang Minquan et al. (2024) found that artificial intelligence technology uses big data to screen out corresponding compounds for molecular simulation, and feeds the simulation results back to the artificial intelligence system for learning, and continuously optimizes the artificial neural network. The combined use of artificial intelligence and molecular simulation technology improves the efficiency of drug design research, reduces the impact of human factors on simulation results, and increases the credibility of simulation results. For example, in the preclinical research stage, artificial intelligence can use machine learning algorithms to accurately predict the biological activity, pharmacokinetic properties, and toxicity of candidate drugs, helping researchers discover potential problems in advance and optimize them, thus improving the quality of drugs. Success rate in entering clinical trials.

In addition, artificial intelligence can also discover biomarkers and risk factors closely related to patient efficacy and safety by mining and analyzing clinical trial data, providing strong support for the design and optimization of clinical trials, and further improving the success rate of drug development.

2 Ethical Considerations in Artificial Intelligence and Drug Design

2.1 Data privacy and security issues

In the process of applying artificial intelligence to drug design, data privacy and security issues are particularly critical. This involves how to reasonably and legally collect, store and use large amounts of biometric data, medical information, patient records and other sensitive content.

The protection of data privacy is a dual ethical and legal requirement. Murdoch (2021) research stated that patients' personal information, genetic data, etc. are highly sensitive information, and once leaked, it may have a serious impact on the patient's life, work, and even personal safety. Therefore, when collecting these data, the patient’s explicit consent must be obtained and their rights to information, choice, and refusal must be fully respected. At the same time, the data storage and transmission process also requires strict encryption to prevent data from being illegally obtained or abused.

Pesapane et al. (2018) analyzed the legal framework regulating medical devices and data protection in Europe and the United States, assessed the developments currently taking place, and stated that data security issues cannot be ignored. During the drug design process, large amounts of data need to be shared and exchanged between different institutions, platforms and even countries. This brings great challenges to data security. On the one hand, it is necessary to establish a complete data sharing mechanism to ensure that data flows under the premise of legality and compliance; on the other hand, it is also necessary to strengthen the supervision of the data sharing process to prevent data from being tampered with, abused or used for other illegal purposes (Zhang et al., 2022).

With the continuous development of artificial intelligence technology, the value and importance of data have become increasingly prominent. This makes data a target for various attacks and theft. Therefore, it is necessary to continuously improve data security protection capabilities and adopt the latest technical means and methods to deal with various network attacks and data leakage incidents.

2.2 Transparency and explainability of artificial intelligence decision-making

In the field of drug design, the transparency and explainability of artificial intelligence decision-making have become the focus of public attention, scientific researchers and regulatory agencies. This is not only because of the advancement of technology, but also because AI decision-making is directly related to human health and life safety.

On August 15, 2021, Professor Liang Zheng, Vice Dean of the Institute of International Governance of Artificial Intelligence at Tsinghua University, attended "The 4th Issue of the Future Forum AI Ethics and Governance Series - Reliability and Explainability of AI Decision-Making". Professor Liang Zheng pointed out that reliable AI should have four major elements: security, fairness, transparency, and privacy protection. Therefore, "trustworthiness" and "explainability" are positively related, especially for users and the public. Implementing algorithm explainability is an important part of ensuring reliability and trust (https://aiig.tsinghua.edu.cn/ info/1296/1328.htm).

Transparency, simply put, refers to the extent to which the processes and logic behind AI decisions can be understood and viewed. In drug design, an AI model might recommend a certain molecular structure as a potential drug candidate based on millions of data points and complex algorithms. But the question is, how are these decisions made? What data is it based on? What algorithms are used? Deng et al. (2022) studied common data resources, molecular representations, and benchmark platforms to decompose artificial intelligence technology into model architectures and learning paradigms. Reflects the technical development of artificial intelligence in drug discovery over the years and provides a GitHub repository containing a series of papers (and code, if applicable) as a learning resource, which is updated regularly. These need to be made clear. Transparency requires that the AI system can provide sufficient information to allow external observers to understand the basis and logic of its decisions.

Explainability goes a step further, requiring AI to not only demonstrate its decision-making process, but also explain the reasons for its decisions in a way that humans can understand. In drug design, this means that AI needs to be able to explain why a certain molecular structure was chosen and not others. This explanation cannot be just "because the algorithm says so", but should be based on specific chemical or biological principles or known experimental results.

However, achieving transparency and explainability is not easy. The decision-making process of AI often involves large amounts of data and complex calculations, which is difficult to describe in simple language. In addition, some AI models themselves are "black box" models, and their internal logic is not easy to understand. Therefore, researchers need to continuously explore new methods and technologies to improve the transparency and explainability of AI decision-making (Schneider et al., 2020).

2.3 Definition of ethical responsibilities of artificial intelligence in drug design

In the field of drug design, the application of artificial intelligence is becoming more and more widespread. However, it is followed by a series of complex ethical issues, the most core of which is the definition of ethical responsibility. The ethical responsibility of artificial intelligence in drug design is not a simple "yes or no", but a complex issue involving multiple levels and requiring careful consideration.

It must be recognized that although artificial intelligence has powerful computing power and data analysis capabilities, it is still a tool based on human programming and algorithms. Therefore, Jing et al. (2018) found that 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.

The 2023 research by Li Shuangxing and others focused on the application of artificial intelligence in the drug discovery process. Through in-depth analysis and empirical research, it revealed the key role of AI technology in accelerating the discovery and optimization of new drugs. This research uses a variety of AI algorithms and models to successfully shorten the drug development cycle and improve research and development efficiency. This important discovery has brought new breakthroughs to the pharmaceutical industry and provided new ideas and directions for future drug research and development (Li et al., 2023).

Data is the fuel of AI, but in the field of drug design, high-quality data is not easy to obtain. Biomedical data are often scattered across different databases and laboratories, and suffer from standardization and annotation issues. In addition, data on rare diseases and emerging diseases are particularly scarce, which limits the training and application of AI models in these fields (Tripathi et al., 2022) . Although AI models perform well on certain tasks, their decision-making process in drug design is often a "black box." This lack of interpretability makes it difficult for researchers to trust model predictions, especially in drug development involving human health and life. At the same time, how to ensure that the decision-making of the AI model complies with ethical principles? How to supervise the drug development process based on AI technology to ensure its safety and effectiveness? These issues need to be faced and resolved jointly by scientific researchers, policymakers and regulatory agencies. As AI technology continues to develop, the relevant ethical and regulatory frameworks also need to be constantly updated and improved.

However, these challenges have not prevented the application of AI in the field of drug design, but have created a large number of opportunities. London (2019) research believes that opaque decisions are more common in medicine than critics realize, and AI technology is changing all aspects of drug design, from target identification to candidate drug screening, optimization and clinical trial design. For example: Huawei Cloud Pangu Drug Molecular Large Model is a large model jointly developed by Huawei Cloud and Shanghai Institute of Materia Medica for the pharmaceutical field. This model can generate billions of new compound databases and improve the performance of multiple drug discovery tasks. It cooperated with the First Affiliated Hospital of Xi'an Jiaotong University to develop broad-spectrum antibacterial drugs, shortening the research and development cycle to one month and significantly improving efficiency. At the same time, the structure optimization function of the model can also help reduce the toxic and side effects of drugs on the human body. This achievement demonstrates the huge potential of artificial intelligence in drug research and development and is expected to bring revolutionary changes to the creation of new drugs.

3.3 Future development strategies for combining artificial intelligence with drug design

In order to fully realize the potential of AI in drug design and cope with related challenges, a reasonable development strategy needs to be formulated. First, it is necessary to strengthen interdisciplinary cooperation and talent cultivation. Drug design involves knowledge and technology from multiple disciplines such as biology, chemistry, computer science, etc. Therefore, it is necessary to cultivate a group of compound talents who understand both drug design and AI technology. This will help promote the application and development of AI technology in drug design. Lee et al. (2022) found that the application of data preprocessing and big data and artificial intelligence methods enables accurate and comprehensive analysis of massive biomedical data, and Developing predictive models in the field of drug design will provide useful information in the era of biomedical big data.

Establish a complete data sharing and intellectual property protection mechanism. Data is the core resource of AI technology, but the acquisition and sharing of data are often subject to many restrictions. Therefore, it is necessary to establish a reasonable data sharing mechanism to promote cooperation and exchanges among scientific researchers; at the same time, strengthen intellectual property protection and data privacy protection to ensure that the legitimate rights and interests of scientific researchers are protected (Noorbakhsh-Sabet et al., 2019).

Continue to pay attention to and track the development of emerging technologies, and apply new technologies to the field of drug design in a timely manner. With the rapid development of science and technology, new AI technologies and algorithms continue to emerge, providing new ideas and methods for drug design. Thomas et al. (2022) believe that it is necessary to maintain sensitivity and foresight to new technologies, apply new technologies in practical work in a timely manner, and promote the in-depth integration and development of AI and drug design.

The combination of artificial intelligence and drug design has broad prospects and huge potential. By strengthening cooperation, improving mechanisms and continuing innovation, we can expect more breakthroughs and results in this field in the future and make greater contributions to human health.

4 Conclusions and Suggestions

4.1 The importance and potential of artificial intelligence in the field of drug design

The application of artificial intelligence in the field of drug design is gradually highlighting its importance and huge potential, bringing unprecedented opportunities to the pharmaceutical industry.

Artificial intelligence has greatly improved the efficiency of drug research and development. The traditional drug research and development process is long and complex, but AI technology can quickly screen and optimize drug candidates through powerful computing power and advanced data analysis methods, significantly shortening the research and development cycle (Muller et al., 2022) . This not only reduces research and development costs, but also allows more potential therapeutic drugs to enter the clinical trial stage, providing patients with more treatment options.

Artificial intelligence improves the precision of drug development. Selvaraj et al. (2022) found that AI can deeply mine information in biological data, accurately identify drug targets and biomarkers, and provide strong support for the formulation of precision medicine and personalized treatment plans. This means that future drugs will be more tailored to patients' individual differences, improve treatment effects, and reduce side effects.

Of course, the potential of artificial intelligence in drug design is far from being fully exploited. With the continuous advancement and innovation of technology, AI is expected to play a greater role in the analysis of drug action mechanisms, the design of multi-target drugs, drug metabolism and toxicity prediction (Thomas et al., 2022). This will further promote innovation and development in drug research and development and bring more breakthroughs to human health.

However, we must also be clearly aware that the application of artificial intelligence in the field of drug design still faces many challenges. Data quality, algorithm reliability, ethical issues, etc. all need to be carefully considered and resolved. Therefore, when promoting the application of AI technology in drug design, we should maintain a prudent and responsible attitude to ensure that technological innovation can truly benefit mankind.

4.2 Ethical issues that need to be considered when facing future technological development

With the in-depth application of artificial intelligence in the field of drug design, a series of accompanying ethical issues have to be faced. These issues are not only related to the healthy development of technology, but also involve the moral bottom line, fairness and justice of human society.

Data privacy and security issues have become urgent problems to be solved. During the drug development process, a large amount of patient data needs to be collected for training and optimization of AI models. This data often contains sensitive information such as patients' personal information and genomic data. Once leaked or misused, it will pose a great threat to patients' privacy and security. Therefore, a sound data protection mechanism must be established to ensure that patients' privacy rights are fully respected and protected (Paul et al., 2021).

Transparency and explainability of AI decision-making have also received much attention. Brown et al. (2020) found that in drug design, AI models are often able to make complex decisions beyond human understanding. However, this "black box" decision-making process lacks transparency and explainability, making it difficult for people to understand and trust AI's decision-making results. This not only affects the widespread application of AI in drug design, but may also trigger public panic and resistance to AI technology. Therefore, it is necessary to continuously explore and improve the transparency and explainability of AI decision-making to enhance the public’s trust in AI technology.

The definition of ethical responsibility is also an issue that cannot be ignored. When AI makes mistakes in drug design or causes adverse consequences, how to define liability becomes a difficult problem. Should AI developers, data providers or users bear the responsibility? This requires in-depth thinking and discussion, and the establishment of a complete responsibility definition mechanism to ensure that all parties can also assume corresponding ethical responsibilities while enjoying the convenience brought by AI technology.

4.3 Prospects for the innovation and ethical balance of artificial intelligence in drug design

Facing the broad prospects of artificial intelligence in the field of drug design, we must realize that innovation and ethics do not exist in isolation, but are interdependent and mutually reinforcing. Future research should be committed to promoting technological innovation while ensuring that ethical principles are adhered to and respected.

Technological innovation is the source of power for sustainable development in the pharmaceutical field, and the application of artificial intelligence brings new possibilities to drug research and development. Scientific researchers should be encouraged to continue to deeply explore the potential of AI in drug design, give full play to its unique advantages in data analysis, pattern recognition, etc., and provide more powerful tools and methods for the development of new drugs (Peña-Guerrero et al., 2021).

However, technological innovation cannot be an excuse to ignore ethics. In the process of promoting AI applications, we must always adhere to the ethical bottom line and ensure that human dignity and rights are not violated. This requires always paying attention to ethical issues such as data privacy and security, decision-making transparency and explainability, and definition of responsibilities in research to ensure that technological innovation moves forward on a legal and compliant track.

To balance the relationship between innovation and ethics, future research should focus on interdisciplinary collaboration and communication. Computer scientists, biomedical experts, jurists and ethicists should jointly participate in the research of AI and drug design, and jointly promote the healthy development of this field through knowledge integration and method innovation (Duch et al., 2019). This kind of interdisciplinary cooperation and exchange not only helps to improve the efficiency and quality of technological innovation, but also ensures that ethical principles are fully respected and reflected in technological innovation.

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