TRPV4-Gemma-3B: A Method for Discovering Transient Receptor Potential Vanilloid 4 Inhibitors Based on Causal Language Model

Transient Receptor Potential Vanilloid 4 (TRPV4) functions as a multimodally activated Ca$^{2+}$ permeable, non-selective cation channel involved in osmotic and mechanical cue transduction, playing a significant role in joint pain and inflammatory responses in Rheumatoid Arthritis (RA). The deep learning-driven approach has already been applied to the research of TRPV4 and its inhibitors. Large language models have demonstrated remarkable capabilities in knowledge transfer and downstream tasks, performing excellently even on small datasets of inhibitors. A fine-tuned model, TRPV4-Gemma-3B, was used to generate candidate molecules for de novo design of TRPV4 inhibitors. Additionally, our candidate screening protocol employs standard evaluation methods in small molecule drug discovery, including assessments of drug-likeness, synthetic accessibility, and target-ligand binding affinity. By combining affinity data and molecular fragments, we compared the differences between the sample data and the content generated by TRPV4-Gemma-3B. This further validated that our fine-tuned model successfully captured the structural characteristics of TRPV4 inhibitor molecules. We further validated the affinity between the molecules designed by TRPV4-Gemma-3B and the target using molecular dynamics methods, which also strengthened the reliability of our fine-tuned Gemma-based tool in redesigning TRPV4 inhibitors. In future work, further integrating reinforcement learning and multi-agent approaches to incorporate the diverse structural characteristics exhibited by inhibitor molecules into the alignment of large models will also be of significant importance.