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探索通过深度学习方法对抗食源性病原体的新型抗菌剂
Huixi Zhang1,2, Shanxue Jiang1,2,3, Haishu Sun1,2,3
1Department of Environmental Science and Engineering, Beijing Technology and Business University, Beijing 100048, China.
Journal of agricultural and food chemistry
|March 13, 2025
概括
深度学习模型确定了安全有效的天然产品,用于对抗抗生素耐药性食品传播的病原体,如大肠杆菌和金黄色葡萄球菌. 这种方法加速了用于食品安全应用的新型抗菌剂的发现.
科学领域:
- 食品科学 食品科学 食品科学
- 微生物学 微生物学
- 计算化学计算化学
背景情况:
- 食物传播病原体的抗生素耐药性对全球健康构成重大风险.
- 新型抗菌剂对于确保食品安全和预防人类感染至关重要.
研究的目的:
- 开发最佳的深度学习模型来预测抗菌活性和致癌性.
- 选天然产品作为抗微生物药物对食源性病原体的潜在用途.
- 为食品工业确定安全有效的天然化合物.
主要方法:
- 利用深度学习来构建抗菌活性和致癌性的预测模型.
- 使用开发的模型选了COCONUT天然产品数据库.
- 通过实验验证了选定的天然产品的抗微生物特性.
主要成果:
- 确定了130种表现出抗菌活性和非致癌性能的天然产品.
- 成功验证了 bis ((hexamethylene) triamine 和 N-phenethylbiguanide 的抗菌疗效.
- 证明了深度学习方法的可靠性和效率.
结论:
- 开发的深度学习模型为发现新型抗菌剂提供了有效的策略.
- 这项研究为确定食品安全应用的安全和有效的天然产品提供了一条途径.
- 这项研究为对抗抗生素耐药性食品传播病原体提供了创新的见解.
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