一个基于LSTM的新型机器学习模型,用于预测食品蛋白衍生的抗高血压的活性
Wang Liao1,2, Siyuan Yan1,2, Xinyi Cao1,2
1Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
Molecules (Basel, Switzerland)
|July 14, 2023
概括
使用长期短期记忆 (LSTM) 算法的新深度学习模型可以有效地预测抗高血压. 这种方法有助于从食品蛋白中选出ACE抑制的生物活性.
科学领域:
- 生物活性类的生物活性.
- 计算化学计算化学
- 食品科学 食品科学 食品科学
背景情况:
- 食品蛋白质衍生的具有抗高血压特性,是生物活性研究的关键领域.
- 之前的结构-活动关系研究使用了部分最小平方回归模型.
- 机器学习为推进生物活性研究提供了潜力.
研究的目的:
- 开发一种新的深度学习模型,用于预测抗高血压的抑制活性.
- 评估模型在预测针对血管酶转化酶 (ACE) 的IC50值方面的性能.
- 建立一种有效的方法来选来自食品蛋白质来源的抗高血压.
主要方法:
- 使用长短期记忆 (LSTM) 算法构建一个深度学习模型.
- 预测半最大抑制度 (IC50) 值用于抑制ACE活性的.
- 使用测试数据集和随机合成的LSTM模型的验证.
主要成果:
- 开发的基于LSTM的深度学习模型在预测ACE抑制的IC50值方面表现出有效性.
- 模型验证证实了它对未见数据的预测能力,包括新序列.
- 该模型提供了一种简化和有效的方法来识别潜在的抗高血压.
结论:
- 基于LSTM的深度学习模型为选抗高血压提供了一种高效和精简的方法.
- 这种方法有助于识别来自食物蛋白质的生物活性,这些具有抑制ACE的潜力.
- 这项研究强调了先进的机器学习技术在生物活性发现领域的有用性.
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