## 3 Results and Discussion
### Interpretation of Assay Results from Physicochemical Descriptors  
From our assay results, we found that TRPA1 agonists concentrated in some clusters categorized by physicochemical molecular descriptors. Clusters 100, 31, and 33 revealed that compounds categorized in P1 and P2 were concentrated (Figure 5). Interestingly, such clusters were not designed by reflecting prior knowledge of TRPA1 agonists; it showed high hit rates of finding agonist candidates.  
In cluster 100, within 17 compound members, all 15 selected candidates exhibited TRPA1 agonist activity, implying that the physicochemical descriptor combination, which defines this cluster, can define some TRPA1 agonist physicochemical rules (Figure 5a). Practically, the cluster 100 members can be described from the cluster property, as compounds with high Manhold LogP and carry amides conjugated to a long Pi chain (Table S2).  
In cluster 31, 8 of 12 compounds exhibited TRPA1 agonist activities. However, compared with cluster 100, the Manhold LogP value of this cluster member compound was low (Figure 5b). The average Manhold LogP value of 31 cluster member compounds was 2.59 (707 compounds average: 3.05, SD: 0.73). The physicochemical property of cluster 31 can be expressed as compounds carrying a high bond polarization ratio and triple bonds.  
In cluster 33, all four compounds exhibited TRPA1 agonist activities (Figure 5c). In that they have triple bonds, the compounds in cluster 33 have a structure similar to cluster 31.  
All compounds in this cluster have two benzene rings and higher Manhold LogP than compounds in cluster 31 (average Manhold LogP in cluster 33: 3.33).  
Our clustering approach led to finding several clusters of TRPA1 agonists categorized by the compounds' total physicochemical profiles, indicating that physicochemical molecular descriptor combinations can define some of the TRPA1 agonists with quantitative data. Such data-driven structure design will help assess or further optimize the agonist candidate with an in silico approach. However, our assay data also indicated that the physicochemical molecular rule was complex. When we attempted to assess the discrimination performance using several major molecular descriptors considered in designing compounds for drugs, it was difficult to clearly explain the structural difference between P1 and N2, although there was a slight discrimination effect for some physicochemical properties, such as Manhold LogP, Largest Chain, and Bond Polalizabilities (Figure 6). Consequently, the results indicated that the structure rules of TRPA1 agonists are complex.  
Figure 5: Representative clusters consist of a high rate of P1 (high activity) and P2 (medium activity). (**a**) Cluster 100, (**b**) cluster 31, and (**c**) cluster 33. Heatmap represents the cluster’s physicochemical profile described by 27 molecular descriptors. The list of molecular descriptors, which is the same as (**a**), is abbreviated in (**b**) and (**c**). The molecular structures of assayed compounds in each cluster are illustrated.  
Our approach, \"image-based TRPA1 assay combined with an in silico chemical space clustering concept\", enabled the collection of new TRPA1 agonist interaction data that can provide new insights for TRPA1 agonists from the perspective of physicochemical molecular properties. Interestingly, the alkyne-type structural rule was identified as a potential new design for potent TRPA1 agonists.