【AI详解】The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies.

文献信息

一句话小结

本文回顾了人工智能在药物发现中的应用，强调其提高效率与准确性的潜力，同时指出高质量数据、伦理问题和方法局限性是成功应用的关键挑战。研究提出了数据增强、可解释性AI及与传统方法结合的策略，为推动AI在制药研究中的实际应用提供了重要见解。

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AI辅助内容生成 · 人工智能的局限性 · 人工智能 · 药物发现

摘要

人工智能（AI）有潜力革新药物发现过程，提供更高的效率、准确性和速度。然而，AI成功应用的前提是高质量数据的可用性、伦理问题的解决以及对基于AI的方法局限性的认识。本文回顾了AI在该领域的优势、挑战和缺陷，并提出了克服当前障碍的可能策略和方法。还讨论了数据增强、可解释性AI以及AI与传统实验方法的结合的使用，以及AI在制药研究中潜在的优势。总体而言，本综述强调了AI在药物发现中的潜力，并为实现其在该领域的潜力提供了挑战和机遇的见解。人类作者的备注：本文旨在测试基于GPT-3.5语言模型的聊天机器人ChatGPT在协助人类作者撰写综述文章方面的能力。根据我们的指示生成的文本（见支持信息）被用作起点，并评估了其自动生成内容的能力。在进行彻底审查后，人类作者几乎重写了手稿，努力保持原始提案与科学标准之间的平衡。在最后一节中讨论了为此目的使用AI的优缺点。

英文摘要

Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in terms of assisting human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, the human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and the scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.

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主要研究问题

1. 在药物发现过程中，如何有效解决AI所依赖的高质量数据的获取问题？
2. 除了伦理问题，AI在药物发现中还面临哪些技术或操作上的挑战？
3. 如何将AI与传统实验方法有效整合，以提高药物发现的成功率？
4. 数据增强在AI药物发现中的具体应用案例有哪些？其效果如何评估？
5. 在药物发现领域，哪些领域的AI应用前景最为广阔，为什么？

核心洞察

研究主题和范围

本综述文章探讨了人工智能（AI）在药物发现过程中的角色，包括其潜在的挑战、机遇和策略。随着药物发现的复杂性和时间成本不断增加，AI技术的引入被认为能够提高效率、准确性和速度。

主要发现和观点

1. AI的潜力：AI能够加速药物发现，通过机器学习（ML）和自然语言处理（NLP）等技术，实现对大量数据的高效分析。
2. 应用案例：多个成功案例表明，AI可以用于预测药物的有效性和毒性，识别药物-药物相互作用，并设计新化合物。
3. 伦理和数据质量问题：AI的有效应用依赖于高质量的数据，且在使用过程中需关注伦理问题，如公平性和偏见。

研究进展

• 技术发展：深度学习（DL）算法的应用在药物发现中取得了显著进展，尤其是在药物活性预测和毒性评估方面。
• 协作模式：AI研究人员与制药科学家的合作被认为是加速药物开发的重要因素，能够提升临床试验的准确性和效率。

争议与不足

• 数据质量：AI方法对数据的依赖使得数据的质量和可用性成为主要挑战，低质量或不一致的数据可能导致不准确的结果。
• 伦理考量：AI在药物开发中的应用可能引发公平性和偏见问题，特别是在算法训练数据的代表性方面。

未来研究方向

1. 数据增强：利用合成数据来补充现有数据集，以提高AI算法的训练效果。
2. 可解释性AI（XAI）：开发可解释的AI方法，以提高算法的透明度和理解度，从而应对偏见和公平性问题。
3. AI与传统方法结合：将AI与传统实验方法结合，以优化药物发现过程，增强其有效性。

关键结果和发现

• AI在药物发现中展现出显著的潜力，能够提高药物的发现速度和安全性。
• 通过对大量已知药物数据的分析，AI算法能够识别新的药物候选分子，并预测其生物活性。

主要结论/意义/创新性

综述强调，AI有潜力彻底改变药物发现过程，但其成功应用依赖于高质量的数据、伦理问题的解决以及对AI方法局限性的认识。未来的研究应集中在克服当前面临的挑战，以实现AI在药物发现中的全面应用。

研究局限性和未来方向

• 文章指出AI在药物发现中的应用仍面临许多挑战，包括数据的可用性和伦理问题。
• 未来的研究应着重于提升数据质量、开发可解释的AI技术，并加强跨学科的合作，以推动药物发现的进步。

参考文献

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引用本文的文献

1. Using AI to write scholarly publications. - Mohammad Hosseini;Lisa M Rasmussen;David B Resnik - Accountability in research (2024)
2. Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma. - Jan Homolak - Croatian medical journal (2023)
3. Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other Large Language Models in scholarly peer review. - Mohammad Hosseini;Serge P J M Horbach - Research square (2023)
4. Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other large language models in scholarly peer review. - Mohammad Hosseini;Serge P J M Horbach - Research integrity and peer review (2023)
5. Using ChatGPT in Medical Research: Current Status and Future Directions. - Suebsarn Ruksakulpiwat;Ayanesh Kumar;Anuoluwapo Ajibade - Journal of multidisciplinary healthcare (2023)
6. Exploring the adoption of ChatGPT in academic publishing: insights and lessons for scientific writing. - Jan Homolak - Croatian medical journal (2023)
7. Examining the Validity of ChatGPT in Identifying Relevant Nephrology Literature: Findings and Implications. - Supawadee Suppadungsuk;Charat Thongprayoon;Pajaree Krisanapan;Supawit Tangpanithandee;Oscar Garcia Valencia;Jing Miao;Poemlarp Mekraksakit;Kianoush Kashani;Wisit Cheungpasitporn - Journal of clinical medicine (2023)
8. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. - Shuroug A Alowais;Sahar S Alghamdi;Nada Alsuhebany;Tariq Alqahtani;Abdulrahman I Alshaya;Sumaya N Almohareb;Atheer Aldairem;Mohammed Alrashed;Khalid Bin Saleh;Hisham A Badreldin;Majed S Al Yami;Shmeylan Al Harbi;Abdulkareem M Albekairy - BMC medical education (2023)
9. Navigating the Landscape of Personalized Medicine: The Relevance of ChatGPT, BingChat, and Bard AI in Nephrology Literature Searches. - Noppawit Aiumtrakul;Charat Thongprayoon;Supawadee Suppadungsuk;Pajaree Krisanapan;Jing Miao;Fawad Qureshi;Wisit Cheungpasitporn - Journal of personalized medicine (2023)
10. GLP-1 Receptor Agonists and Related Mental Health Issues; Insights from a Range of Social Media Platforms Using a Mixed-Methods Approach. - Davide Arillotta;Giuseppe Floresta;Amira Guirguis;John Martin Corkery;Valeria Catalani;Giovanni Martinotti;Stefano L Sensi;Fabrizio Schifano - Brain sciences (2023)

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