We pre-trained our model on PubChem-10M dataset and fine-tuned it on collected TRPV4 inhibitors, which we refer to as templates. We used 711 template molecules as the training dataset for fine-tuning and generated 813 novel molecules which are valid and unique. We can generate as many molecules as we want, but to facilitate the comparison of properties between the novel molecules and the templates, we kept the number of generated novel molecules approximately consistent with the number of templates. The chemical distributions of the template molecules and generated molecules are visually plotted in Figure . The distribution of the template molecules is consistent with that of the generated molecules, demonstrating our model's ability to generate molecules within a specific chemical space. As shown by the t-SNE plots in Figure , the molecules generated by our TRPV4-Gemma overlap almost completely with the existing inhibitors. The PCA plot shows that the distribution of the generated molecules is approximately consistent with that of the existing molecules, but there are internal differences that demonstrate the novelty of the generated molecules. We calculated the QED and SA of the generated molecules to demonstrate their properties and the performance of our model. Figure shows the QED distribution of the template molecules and generated molecules. The density of the template molecules is higher in the 0.6 to 0.8 QED score range, while the generated molecules have a higher density in the 0.2 to 0.6 range. It is worth noting that in the highest QED range (>0.8), the generated molecules have a higher density. The broader distribution of QED scores in the generated dataset showcases the model's ability to explore a wider chemical space. This diversity is beneficial for drug discovery as it introduces new molecular structures that might not be present in the template dataset. The generated molecules' ability to cover a broader range of QED scores demonstrates the potential for identifying unique and novel compounds with desirable drug-like properties. In Figure , there is significant overlap between the two distributions of SA, indicating that the generated molecules share some synthetic accessibility characteristics with the template molecules. However, the shift in peak locations and densities highlights the differences in synthetic accessibility between the generated molecules and the template molecules.