药物发现中的植物化学物质 - - 传统和创新的融合
Patience Chihomvu1, A Ganesan2, Simon Gibbons3
1Compound Synthesis and Management, Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, 431 83 Mölndal, Sweden.
International journal of molecular sciences
|August 29, 2024
概括
通过先进的分析和计算方法,发现用于药物开发的天然化合物得到了增强. 机器学习和网络药理学预测了植物化学标,加速了生物活性标的识别.
科学领域:
- 自然产品化学 自然产品化学
- 计算机化药物发现.
- 生物信息学是一种生物信息学.
背景情况:
- 植物化学物质在药物发现方面有着悠久的历史.
- 分析技术的进步简化了生物活性天然化合物的识别.
- 计算工具对于自然产品研究越来越重要.
研究的目的:
- 突出现代分析和计算技术在植物化学药物发现中的作用.
- 探索计算方法如何指导自然产品的实验验证.
- 展示用于标识的结构生物学和计算生物学的整合.
主要方法:
- 使用先进的分析技术,如液体染色学-质谱学 (LC-MS) 和液体染色学-核磁共振 (LC-NMR) 来识别化合物.
- 使用计算策略,包括分子对接,定量结构-活动关系 (QSAR) 建模,机器学习 (ML) 和网络药理学.
- 整合结构生物学和计算方法用于目标预测和相互作用分析.
主要成果:
- 现代分析方法简化了植物化学品的识别和表征.
- 像分子对接,QSAR,ML和网络药理学这样的计算技术可以预测天然产品的目标.
- 这些方法的整合加快了生物活性线索的发现,并提供了对植物化学物质-目标相互作用的见解.
结论:
- 植物化学物质仍然是药物发现的关键来源.
- 先进的分析和计算工具显著提高了识别和验证生物活性天然化合物的效率和准确性.
- 机器学习,特别是QSAR和深度神经网络,在将植物化学性质与生理活动 (如抗微生物和抗癌作用) 相关联方面非常有前途.
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