结合性亲和力预测和针对Phytophthora sojae的农药查使用异质相互作用图表注意力基于网络的模型
Youxu Dai1, Aiping Han1, Huijun Ma2
1School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing 100048, China.
Journal of chemical information and modeling
|February 26, 2025
概括
一个新的图形神经网络模型确定了大豆根腐烂的潜在农药. 来自Schizonepeta草药的MOL011832和MOL011833化合物显示出有希望的结果,通过生物实验验证.
科学领域:
- 农业科学 农业科学
- 计算化学计算化学
- 生物技术是生物技术.
背景情况:
- 大豆的根和茎腐烂导致了全球经济的重大损失.
- 开发有效和新型农药对于作物保护至关重要.
研究的目的:
- 开发和验证一种机器学习模型,用于发现针对Phytophthora sojae.的新农药.
- 从自然来源中识别潜在的候选药物,以控制大豆根腐.
主要方法:
- 使用PDBbind数据构建了一个异质交互图注意力网络模型.
- 从传统中医药系统药理学数据库中选了14249种化合物,用于对抗Phytophthora sojae奇合成酶 (PsChs1).
- 通过分子对接,分子动力学模拟和生物测试使用Schizonepeta提取物验证的顶级候选者 (MOL011832,MOL011833).
主要成果:
- 确定了MOL011832和MOL011833作为潜在的杀虫剂,针对PsChs1.
- 这两种化合物都在草药Schizonepeta中发现.
- 斯基佐内佩塔的乙醇提取物显示出已证实对P. sojae.有抑制作用.
结论:
- 基于图形神经网络的模型是有效和高效的农药发现.
- 斯基佐内佩塔 (Schizonepeta) 含有可能控制大豆根和茎腐烂的化合物.
- 这种方法加速了新型农作物保护剂的识别.
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