构建Kokumi数据库和基于机器学习的预测:对Kokumi分析的系统计算研究
Yi He1, Kaifeng Liu1, Xiangyu Yu1
1Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun 130012, China.
Journal of chemical information and modeling
|January 17, 2024
概括
计算方法加速了kokumi化合物的发现. 机器学习模型可以准确地预测kokumi分子,从而实现高吞吐量选和KokumiPD数据库和预测平台的推出.
科学领域:
- 食品科学与技术 食品科学与技术
- 计算化学计算化学
- 感官科学 感官科学
背景情况:
- Kokumi 是一种充满和厚度的味觉,传统上需要艰苦的分析.
- 新兴的计算方法为分子味道预测提供了有效的策略.
- 开发预测模型对于加速发现kokumi化合物至关重要.
研究的目的:
- 使用计算方法全面分析,预测和选kokumi化合物.
- 根据分子特征对kokumi化合物进行分类,并预测它们的味道特征.
- 通过大规模的虚拟查来识别新的kokumi活性化合物.
主要方法:
- 根据分子特征将285种kokumi化合物分为五组.
- 使用六种结构-味道关系模型 (MLP-E3FP,MLP-PLIF,MLP-RDKFP,SVM-RDKFP,RF-RDKFP,WeaveGNN) 预测kokumi/非kokumi和多种风味的组成.
- 高通量虚拟选超过1亿个分子,随后是毒性和相似性过.
主要成果:
- 编织GNN模型在kokumi/non-kokumi预测中实现了0.94的AUC,超过了其他模型.
- MLP-E3FP模型在多种口味预测方面表现出高的预测性能 (AUC 0.94,MCC 0.74).
- 通过广泛的虚拟选成功识别了kokumi活性化合物.
结论:
- 计算模型,特别是WeaveGNN和MLP-E3FP,在预测kokumi分子方面表现出很高的熟练程度.
- 开发的平台,KokumiPD,为kokumi化合物分析和预测提供了宝贵的资源.
- 这项研究显著提高了kokumi化合物发现和应用的效率.
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