数字农科院2.0

Leveraging computer-aided drug design for the discovery of phytohormone analogs: A review

文献类型： 外文期刊

作者： Yuling Guo;Ghulam Qanmber;Zhao Liu;Han Yang;Jun Li;Zhikun Yang;Zhongxian Li;Linlin Yang;Zuoren Yang

作者机构：

关键词： Abscisic acid;Artificial intelligence (AI);Brassinolide;Computer-aided drug design (CADD);Phytohormone analog

期刊名称： Plant Stress

ISSN： 2667-064X

年卷期： 2025 年 17 卷

页码：

收录情况： ESCI（2025版）

摘要： Natural phytohormones regulate plant growth, development, and stress responses, but discovering novel analogs is challenging due to complex signaling pathways, limited structural diversity, and the high cost of conventional methods. Computer-aided drug design (CADD) has emerged as a vital tool in modern phytohormone research, expediting lead compound discovery and facilitating the exploration of new phytohormone analogs. This review delves into the definition, historical evolution, key software, and representative examples of CADD applications. Notably, it provides the first comprehensive analysis of CADD's role in identifying analogs of phytohormone, including auxin, gibberellin (GA), cytokinin (CTK), abscisic acid (ABA), ethylene (ETH), and brassinolide (BR), as well as its broader applications in the study of stress-related plant metabolites. Key computational methodologies such as structure-activity relationship (SAR) analysis, molecular docking, and molecular dynamics (MD) simulations have become central to the rational design of new phytohormone analogs. Most studies focus on developing receptor-targeted agonists or antagonists, with some efforts given to their synthesis or metabolic pathways. Among the six primary phytohormones, ABA analogs are the most extensively studied, resulting in highly efficient and versatile compounds. In contrast, BR analogs remain underexplored, despite their significant agricultural potential. The pursuit of phytohormone analogs via CADD, however, faces significant challenges. These include the need for more accurate models and advanced algorithms to predict hormone-receptor interactions and metabolic pathways. Nevertheless, emerging technologies such as quantum computing and artificial intelligence (AI) promise to enhance the precision and efficiency of predictive modeling and simulations, paving the way for breakthroughs in phytohormone research.

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