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Abstract：
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.
摘要：
由于气味感知空间的复杂和潜在的不连续性质，预测气味分子的嗅觉感知具有挑战性。在这项研究中,我们介绍了一个深度学习模型，Mol-PECO（库仑矩阵位置编码的分子表示），旨在基于分子结构和静电来预测嗅觉感知。Mol-PECO通过利用库仑矩阵学习分子的有效嵌入，编码原子坐标和电荷，作为邻接矩阵及其拉普拉斯特征函数的替代形式，作为原子的位置编码。有了气味分子和描述符的全面数据集，Mol-PECO优于使用分子指纹和基于邻接矩阵的图神经网络的传统机器学习方法。Mol-PECO学习的嵌入有效地捕获了气味空间，实现描述符的全局聚类和相似气味的局部检索。这项工作有助于更深入地理解嗅觉及其机制。
