用分子振动预测气味特征，突破结构决定气味的传统认知
Motional representation; the ability to predict odor characters using molecular vibrations
- 构建基于分子振动参数的CNN回归模型，直接从振动特征预测气味
- 振动特征与分子指纹在气味预测上表现相当，证明振动信息有效
- 为气味感知机制提供新视角，适合嗅觉、化学与生成模型研究者
目前仍无法仅凭气味分子结构预测其气味特征。本文设计了一个基于卷积神经网络的回归模型（CNN_vib），用于分析分子振动的计算参数，探究分子振动在气味预测中的能力。研究比较了三种方法：(i) 使用分子振动参数的CNN模型，(ii) 基于振动光谱的逻辑回归，(iii) 基于分子指纹的逻辑回归。结果表明，(i) 和 (ii) 均具有预测能力，且其预测趋势与 (iii) 几乎一致。在不同气味描述符下，(i) 和 (ii) 的预测性能与 (iii) 相当。研究证明，气味不仅可由分子形状决定，也能通过分子振动特征预测。这一发现揭示了超越分子结构的分子运动特征表征潜力。
原文摘要 · Abstract (English)
The prediction of odor characters is still impossible based on the odorant molecular structure. We designed a CNN-based regressor for computed parameters in molecular vibrations (CNN\_vib), in order to investigate the ability to predict odor characters of molecular vibrations. In this study, we explored following three approaches for the predictability; (i) CNN with molecular vibrational parameters, (ii) logistic regression based on vibrational spectra, and (iii) logistic regression with molecular fingerprint(FP). Our investigation demonstrates that both (i) and (ii) provide predictablity, and also that the vibrations as an explanatory variable (i and ii) and logistic regression with fingerprints (iii) show nearly identical tendencies. The predictabilities of (i) and (ii), depending on odor descriptors, are comparable to those of (iii). Our research shows that odor is predictable by odorant molecular vibration as well as their shapes alone. Our findings provide insight into the representation of molecular motional features beyond molecular structures.
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