Figure (a) shows the molecular results of the TPRV4 template inhibitors. Pearson correlation coefficients and p-values between substructures and affinity were calculated to discover substructures that contribute significantly to high affinity. Our analysis ensured that all p-values were less than 0.05, confirming the reliability of the statistical results. Ultimately, we established a correlation map between molecular fragments and binding affinity. We visualized the 20 molecules with the highest Pearson correlation coefficients (shown in blue) and the 20 with the lowest (shown in red). Similarly, Figure (b) shows the analysis results for the content generated by the fine-tuned Gemma model. Through a careful comparison of the data in Figure , we aim to systematically assess the molecular performance of TRPV4-Gemma-3B. We intuitively observe that the number of molecular fragments in the red region of Figure is zero. This indicates that our model has not mistakenly classified important fragments in the sample as structures that do not contribute to binding affinity. Furthermore, there is a 50% overlap between the fragments in the model-designed molecules that do not contribute to binding affinity and the corresponding fragments in the sample (as shown in the purple region of Figure ). These findings suggest that the fine-tuned Gemma-3B possesses a certain degree of structural knowledge regarding TRPV4 inhibitor molecules.