School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore.
School of Humanities, Nanyang Technological University, Singapore 639818, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore.
Drug Discov Today. 2022 Aug;27(8):2235-2243. doi: 10.1016/j.drudis.2022.05.009. Epub 2022 May 14.
Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the large amount of generated data is complex and difficult to analyze effectively. This limitation is increasingly surmounted by artificial intelligence (AI) techniques but more needs to be done. Here, we present and discuss two crucial issues limiting NP-AI drug discovery: the first is on knowledge and resource development (data integration) to bridge the gap between NPs and functional or therapeutic effects. The second issue is on NP-AI modeling considerations, limitations and challenges.
天然产物 (NPs) 是生物活性化合物的重要来源，可用于药物开发。高通量技术的最新进展有助于治疗效果的功能分析和基于 NP 的药物发现。然而，生成的数据量庞大且复杂，难以有效分析。人工智能 (AI) 技术在一定程度上克服了这一局限性，但仍需要进一步研究。在这里，我们提出并讨论了限制 NP-AI 药物发现的两个关键问题：第一个问题是关于知识和资源开发 (数据集成) ，以弥合 NPs 与功能或治疗效果之间的差距。第二个问题是关于 NP-AI 建模的考虑因素、局限性和挑战。
