人工智能驱动的天然产品药物研发

Feng-Lei Duan , Chun-Bao Duan , Hui-Lin Xu , Xin-Ying Zhao , Otgonpurev Sukhbaatar , Jie Gao , Ming-Zhi Zhang , Wei-Hua Zhang , Yu-Cheng Gu
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引用次数: 0

摘要

Michael W. Mullowney 和合著者在《自然-药物发现评论》(Nature Reviews Drug Discovery)上发表的最新综述重点介绍了人工智能技术(特别是机器学习)在天然产物药物发现中的应用。作者讨论了人工智能在这一领域的各种应用,如基因组和代谢组挖掘、天然产物的结构表征以及预测这些化合物的靶点和生物活性。他们还强调了与创建和管理用于训练算法的大型数据集相关的挑战,以及解决这些障碍的策略。此外,作者还探讨了算法训练中的常见误区,并提出了避免这些误区的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

AI-driven drug discovery from natural products

AI-driven drug discovery from natural products

The latest review published in Nature Reviews Drug Discovery by Michael W. Mullowney and co-authors focuses on the use of artificial intelligence techniques, specifically machine learning, in natural product drug discovery. The authors discussed various applications of AI in this field, such as genome and metabolome mining, structural characterization of natural products, and predicting targets and biological activities of these compounds. They also highlighted the challenges associated with creating and managing large datasets for training algorithms, as well as strategies to address these obstacles. Additionally, the authors examine common pitfalls in algorithm training and offer suggestions for avoiding them.

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