Lee, J.; Yoon, H.; Lee, Y.J.; Kim, T.-Y.; Bahn, G.; Kim, Y.-h.; Lim, J.-M.; Park, S.-W.; Song, Y.-S.; Kim, M.-S.;
et al. Drug–Target Interaction Deep Learning-Based Model Identifies the Flavonoid Troxerutin as a Candidate TRPV1 Antagonist. Appl. Sci. 2023, 13, 5617.
https://doi.org/10.3390/app13095617
AMA Style
Lee J, Yoon H, Lee YJ, Kim T-Y, Bahn G, Kim Y-h, Lim J-M, Park S-W, Song Y-S, Kim M-S,
et al. Drug–Target Interaction Deep Learning-Based Model Identifies the Flavonoid Troxerutin as a Candidate TRPV1 Antagonist. Applied Sciences. 2023; 13(9):5617.
https://doi.org/10.3390/app13095617
Chicago/Turabian Style
Lee, Jinyong, Hyunjun Yoon, Youn Jung Lee, Tae-Yoon Kim, Gahee Bahn, Young-heon Kim, Jun-Man Lim, Sang-Wook Park, Young-Sook Song, Mi-Sun Kim,
and et al. 2023. "Drug–Target Interaction Deep Learning-Based Model Identifies the Flavonoid Troxerutin as a Candidate TRPV1 Antagonist" Applied Sciences 13, no. 9: 5617.
https://doi.org/10.3390/app13095617
APA Style
Lee, J., Yoon, H., Lee, Y. J., Kim, T.-Y., Bahn, G., Kim, Y.-h., Lim, J.-M., Park, S.-W., Song, Y.-S., Kim, M.-S., & Beck, B. R.
(2023). Drug–Target Interaction Deep Learning-Based Model Identifies the Flavonoid Troxerutin as a Candidate TRPV1 Antagonist. Applied Sciences, 13(9), 5617.
https://doi.org/10.3390/app13095617
