ArticleACS applied materials & interfaces2025
Harnessing Explainable AI to Explore Structure-Activity Relationships in Artificial Olfaction.
Article in ACS applied materials & interfaces, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical sensor arrays mimic the mammalian olfactory system to achieve artificial olfaction, and receptor materials resembling olfactory receptors are being actively developed. To realize practical artificial olfaction, it is essential to provide guidelines for developing effective receptor materials based on the structure-activity relationship. In this study, we demonstrated the visualization of the relationship between sensing signal features and odorant molecular features using an explainable AI (XAI) technique. We focused on classification tasks and employed a convolutional neural network (CNN) and score-class activation mapping (Score-CAM) methods. The results obtained from analyzing the 94 odor samples prepared using pure solvents indicate that the information regarding the active receptor materials and data points in the signals and the structure-activity relationship could be accurately extracted. Therefore, using XAI techniques to analyze sensor signals from odor data is an important technique for advancing artificial olfaction.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.