Zhang Mengji, Hiki Yusuke, Funahashi Akira, Kobayashi Tetsuya J
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
NPJ Syst Biol Appl. 2024 Jul 17;10(1):76. doi: 10.1038/s41540-024-00401-0.
Predicting olfactory perceptions from odorant molecules is challenging due to the complex and potentially discontinuous nature of the perceptual space for smells. In this study, we introduce a deep learning model, Mol-PECO (Molecular Representation by Positional Encoding of Coulomb Matrix), designed to predict olfactory perceptions based on molecular structures and electrostatics. Mol-PECO learns the efficient embedding of molecules by utilizing the Coulomb matrix, which encodes atomic coordinates and charges, as an alternative of the adjacency matrix and its Laplacian eigenfunctions as positional encoding of atoms. With a comprehensive dataset of odor molecules and descriptors, Mol-PECO outperforms traditional machine learning methods using molecular fingerprints and graph neural networks based on adjacency matrices. The learned embeddings by Mol-PECO effectively capture the odor space, enabling global clustering of descriptors and local retrieval of similar odorants. This work contributes to a deeper understanding of the olfactory sense and its mechanisms.
由于嗅觉感知空间具有复杂且可能不连续的性质，从气味分子预测嗅觉感知具有挑战性。在本研究中，我们引入了一种深度学习模型Mol-PECO（基于库仑矩阵位置编码的分子表示），旨在基于分子结构和静电学来预测嗅觉感知。Mol-PECO通过利用库仑矩阵来学习分子的有效嵌入，库仑矩阵对原子坐标和电荷进行编码，以此替代邻接矩阵，并将其拉普拉斯特征函数用作原子的位置编码。借助一个包含气味分子和描述符的综合数据集，Mol-PECO优于使用分子指纹的传统机器学习方法以及基于邻接矩阵的图神经网络。Mol-PECO学习到的嵌入有效地捕捉了气味空间，能够对描述符进行全局聚类并对相似气味剂进行局部检索。这项工作有助于更深入地理解嗅觉及其机制。
