用AI筛选榕树植物中的降糖化合物，加速新药研发。
Artificial Intelligence Powered Identification of Potential Antidiabetic Compounds in Ficus religiosa
- 结合机器学习与分子对接，智能筛选榕树活性成分。
- 黄酮类和生物碱对糖尿病关键酶DPP-4有强结合力。
- 方法可推广至天然药物发现，适合药物研发人员参考。
糖尿病是一种慢性代谢疾病，其缓慢进展及多种代谢并发症促使亟需新型疗法。研究表明，榕树（Ficus religiosa）作为传统药用植物，可产生具有潜在降糖作用的生物活性植物化学物。本研究采用基于生态系统的计算方法，结合人工智能技术，系统评估榕树中抗糖尿病化合物的潜力。通过整合机器学习、分子对接（AutoDock）及ADMET预测等手段，评估植物化学物对关键靶点二肽基肽酶-4（DPP-4）的抑制效果。DeepBindGCN与AutoDock协同分析结合机制，结果表明黄酮类与生物碱类化合物表现出优异的结合亲和力和有利药理特性。AI的应用显著提升了筛选效率与准确性，为天然产物抗糖尿病药物的研发提供了科学依据，并支持后续实验验证。
原文摘要 · Abstract (English)
Diabetes mellitus is a chronic metabolic disorder that necessitates novel therapeutic innovations due to its gradual progression and the onset of various metabolic complications. Research indicates that Ficus religiosa is a conventional medicinal plant that generates bioactive phytochemicals with potential antidiabetic properties. The investigation employs ecosystem-based computational approaches utilizing artificial intelligence to investigate and evaluate compounds derived from Ficus religiosa that exhibit antidiabetic properties. A comprehensive computational procedure incorporated machine learning methodologies, molecular docking techniques, and ADMET prediction systems to assess phytochemical efficacy against the significant antidiabetic enzyme dipeptidyl peptidase-4 (DPP-4). DeepBindGCN and the AutoDock software facilitated the investigation of binding interactions via deep learning technology. Flavonoids and alkaloids have emerged as attractive phytochemicals due to their strong binding interactions and advantageous pharmacological effects, as indicated by the study. The introduction of AI accelerated screening procedures and enhanced accuracy rates, demonstrating its efficacy in researching plant-based antidiabetic agents. The scientific foundation now facilitates future experimental validation of natural product therapies tailored for diabetic management.
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