The olfactory system employs responses of an ensemble of odorant receptors (ORs) to sense molecules and to generate olfactory percepts. Here we hypothesized that ORs can be viewed as 3D spatial filters that extract molecular features relevant to the olfactory system, similarly to the spatio-temporal filters found in other sensory modalities. To build these filters, we trained a convolutional neural network (CNN) to predict human olfactory percepts obtained from several semantic datasets. Our neural network, the DeepNose, produced responses that are approximately invariant to the molecules' orientation, due to its equivariant architecture. Our network offers high-fidelity perceptual predictions for different olfactory datasets. In addition, our approach allows us to identify molecular features that contribute to specific perceptual descriptors. Because the DeepNose network is designed to be aligned with the biological system, our approach predicts distinct perceptual qualities for different stereoisomers. The architecture of the DeepNose relying on the processing of several molecules at the same time permits inferring the perceptual quality of odor mixtures. We propose that the DeepNose network can use 3D molecular shapes to generate high-quality predictions for human olfactory percepts and help identify molecular features responsible for odor quality.

翻译：嗅觉系统利用一组气味受体（ORs）的响应来感知分子并产生嗅觉感知。在此，我们假设ORs可被视为提取与嗅觉系统相关分子特征的三维空间滤波器，类似于在其他感觉模态中发现的时空滤波器。为构建这些滤波器，我们训练了一个卷积神经网络（CNN）来预测从多个语义数据集中获得的人类嗅觉感知。我们的神经网络——DeepNose，由于其等变架构，产生的响应近似于对分子方向保持不变。我们的网络为不同的嗅觉数据集提供了高保真度的感知预测。此外，我们的方法使我们能够识别对特定感知描述符有贡献的分子特征。由于DeepNose网络的设计与生物系统保持一致，我们的方法预测了不同立体异构体具有不同的感知品质。DeepNose依赖同时处理多个分子的架构，允许推断气味混合物的感知品质。我们提出，DeepNose网络可以利用三维分子形状来生成高质量的人类嗅觉感知预测，并帮助识别决定气味品质的分子特征。