The human brain encodes stimuli from the environment into representations that form a sensory perception of the world. Despite recent advances in understanding visual and auditory perception, olfactory perception remains an under-explored topic in the machine learning community due to the lack of large-scale datasets annotated with labels of human olfactory perception. In this work, we ask the question of whether pre-trained transformer models of chemical structures encode representations that are aligned with human olfactory perception, i.e., can transformers smell like humans? We demonstrate that representations encoded from transformers pre-trained on general chemical structures are highly aligned with human olfactory perception. We use multiple datasets and different types of perceptual representations to show that the representations encoded by transformer models are able to predict: (i) labels associated with odorants provided by experts; (ii) continuous ratings provided by human participants with respect to pre-defined descriptors; and (iii) similarity ratings between odorants provided by human participants. Finally, we evaluate the extent to which this alignment is associated with physicochemical features of odorants known to be relevant for olfactory decoding.

翻译：人类大脑将环境中的刺激编码为表征，从而形成对世界的感官知觉。尽管在理解视觉和听觉感知方面取得了最新进展，但由于缺乏标注人类嗅觉感知标签的大规模数据集，嗅觉感知在机器学习领域仍是一个探索不足的课题。在本研究中，我们探讨了基于化学结构预训练的Transformer模型所编码的表征是否与人类嗅觉感知相一致的问题，即Transformer能否像人类一样感知气味？我们证明，基于通用化学结构预训练的Transformer所编码的表征与人类嗅觉感知高度一致。我们使用多个数据集和不同类型的感知表征来表明，Transformer模型编码的表征能够预测：（i）专家提供的与气味物质相关的标签；（ii）人类参与者针对预定义描述符给出的连续评分；以及（iii）人类参与者提供的气味物质间相似度评分。最后，我们评估了这种一致性与已知对嗅觉解码相关的嗅觉物质物理化学特征之间的关联程度。