品尝品的分类:基于深度学习的方法
Prantar Dutta1, Deepak Jain1, Rakesh Gupta1
1Physical Sciences Research Area, Tata Research Development and Design Centre, TCS Research, 54-B, Hadapsar Industrial Estate, Pune, 411013, India.
Molecular informatics
|October 27, 2023
概括
深度学习模型准确地预测分子味道 (甜,苦,乌玛米),这对于食品和药物设计至关重要. 图形神经网络提供结构洞察力,而不需要手动的特征工程,有助于美味的发现.
科学领域:
- 计算化学是一种计算化学.
- 分子建模分子建模
- 化学信息学 化学信息学
背景情况:
- 预测分子味道对于食品,口味和制药行业至关重要.
- 与G蛋白结合的受体中介于基本口味的感觉:甜,苦,和umami.
- 开发in-silico方法加速了新品品味的设计和选.
研究的目的:
- 开发和评估深度学习模型来分类甜味,苦味和乌玛米分子.
- 探索分子描述符和图形神经网络对于味道预测的实用性.
- 应用可解释的AI技术来理解模型预测和展示实际应用.
主要方法:
- 策划了1466种苦味,1764种甜味和238种乌玛米品味的广泛数据集.
- 使用分子描述符和图形神经网络 (GNN) 训练了一个深度神经网络 (DNN).
- 使用专门的抽样技术解决了类不平衡,并使用了Shapley添加式解释 (SHAP) 来解释模型的可解释性.
主要成果:
- 无论是DNN和GNN模型,在味道预测方面都取得了可比的表现.
- 该GNN模型展示了从分子结构直接学习表示的能力.
- SHAP分析为DNN模型的预测提供了洞察力,增强了理解.
- 模型成功地应用于从大型食品数据库中选品尝者.
结论:
- 开发了有效的in-silico深度学习模型,用于预测分子味道 (甜味,苦味,乌玛米).
- 突出了GNN在从分子结构学习中没有手工制作的特征的优势.
- 该研究提供了一个强大的计算工具,可以加速美味的设计和发现.
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