[论文解读] The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

Alexandre Blanco-González, Alfonso Cabezon+5 more|arXiv (Cornell University)|Dec 8, 2022

Machine Learning in Materials Science被引用 44

一句话总结

本文综述了人工智能如何改变药物发现，概述了收益、挑战、伦理关注，以及诸如数据增强和可解释性AI等策略，并探讨与实验方法的整合。

ABSTRACT

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, to assist 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, human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.

研究动机与目标

• 评估人工智能如何提高药物发现的效率、准确性和速度。
• 识别阻碍AI部署的数据相关、伦理和方法论挑战。
• 讨论克服障碍的策略，包括数据增强和可解释性AI。
• 探讨AI与药物研究中传统实验工作流程的整合。

提出的方法

• 关于药物发现中AI的收益、挑战和弊端的文献综述。
• 讨论数据质量、伦理考量和AI的局限性。
• 提出如数据增强、可解释性AI以及AI与实验整合等策略。

实验结果

研究问题

• RQ1 将AI应用于药物发现的潜在收益与局限性是什么？
• RQ2 为实现有效的AI部署，必须解决哪些数据相关和伦理挑战？
• RQ3 哪些策略可以克服当前障碍（如数据增强、可解释性AI、与实验的整合）？

主要发现

• AI有潜力提高药物发现的效率、准确性和速度。
• 成功部署AI取决于高质量的数据以及解决伦理问题。
• 应将AI与传统实验方法结合以最大化价值。
• 讨论了数据增强和可解释性AI等策略，以克服障碍。
• 本文讨论了将AI用于撰写和研究辅助的优点与局限性。
• 研究强调AI生成的内容与人类科学标准之间的平衡方法。