He Yi, Huang Ruirui, Zhang Ruoyu, He Fei, Han Lu, Han Weiwei
Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China.
Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
iScience. 2024 May 21;27(6):110041. doi: 10.1016/j.isci.2024.110041. eCollection 2024 Jun 21.
Compared to traditional methods, using machine learning to assess or predict the odor of molecules can save costs in various aspects. Our research aims to collect molecules with coffee odor and summarize the regularity of these molecules, ultimately creating a binary classifier that can determine whether a molecule has a coffee odor. In this study, a total of 371 coffee-odor molecules and 9,700 non-coffee-odor molecules were collected. The Knowledge-guided Pre-training of Graph Transformer (KPGT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP), and message-passing neural networks (MPNN) were used to train the data. The model with the best performance was selected as the basis of the predictor. The prediction accuracy value of the KPGT model exceeded 0.84 and the predictor has been deployed as a webserver PredCoffee.
与传统方法相比，使用机器学习来评估或预测分子的气味可以在各个方面节省成本。我们的研究旨在收集具有咖啡气味的分子并总结这些分子的规律，最终创建一个可以确定分子是否具有咖啡气味的二元分类器。在本研究中，总共收集了371个具有咖啡气味的分子和9700个不具有咖啡气味的分子。使用图变换器的知识引导预训练（KPGT）、支持向量机（SVM）、随机森林（RF）、多层感知器（MLP）和消息传递神经网络（MPNN）对数据进行训练。选择性能最佳的模型作为预测器的基础。KPGT模型的预测准确率值超过0.84，并且该预测器已作为网络服务器PredCoffee进行部署。
