To more convincingly verify whether the molecules designed by our model meet the criteria for inhibitor molecules, we employed molecular dynamics to simulate the binding modes between the model-generated molecules and the TRPV4 target. As shown in Figures and, we can clearly see that the model-generated molecules coordinate well with the TRPV4 target. In this process, several fragments within the candidate molecules, as depicted in Figure , interact with the target, including the structural fragments shown in Figures and. However, the substructure fragment shown in Figure has already been identified as an error in our analyses in Figures and. TRPV4-Gemma-3B incorrectly recognized a fragment from the sample data, which does not contribute directly to binding affinity, as a part that does contribute to affinity. This may potentially be due to a certain structural correlation between the fragment in Figure and the structure shown in Figure . In our future research, we will apply reinforcement learning algorithms, using molecules containing the Figure fragment as a rejected dataset to enhance the overall performance of TRPV4-Gemma-3B.