On the alignment between chemical foundation models and neural processing of olfactory perception

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Abstract
Transformer models pre-trained on sensory stimuli such as vision and audition have been shown to capture neural representations and to effectively predict brain responses to corresponding stimuli. Olfactory perception, however, remains a relatively underexplored sensory modality in this context. Understanding the alignment between chemical transformer models and brain activity during olfactory processing is particularly challenging due to the distributed nature of olfactory neural processing and the inherently subjective character of olfactory experience, which is strongly shaped by individual memory and prior exposure. In this work, we evaluate the representational alignment between pre-trained transformer models of chemical structure and functional magnetic resonance imaging recordings collected from human participants during olfactory perception. Using voxel-wise encoding models, we show that representations extracted from state-of-the-art molecular transformers exhibit strong alignment with olfactory brain responses, often exceeding handcrafted molecular fingerprints and physicochemical descriptors. We further analyze how this alignment varies across brain regions, model layers, and temporal response components, revealing distinct region-specific and temporal patterns. These findings represent a step toward understanding how that pre-trained chemical transformers capture meaningful structure relevant to human olfactory perception and provide insight into how molecular features relate to neural processing in the olfactory system.
Funding Information
  • Vetenskapsrådet (2022-06725)
  • Knut och Alice Wallenbergs Stiftelse
  • ERC Syn D2Smell (101118977)

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