Morales, N.; Valdés-Muñoz, E.; González, J.; Valenzuela-Hormazábal, P.; Palma, J.M.; Galarza, C.; Catagua-González, Á.; Yáñez, O.; Pereira, A.; Bustos, D.
Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria. Int. J. Mol. Sci. 2024, 25, 4303.
https://doi.org/10.3390/ijms25084303
AMA Style
Morales N, Valdés-Muñoz E, González J, Valenzuela-Hormazábal P, Palma JM, Galarza C, Catagua-González Á, Yáñez O, Pereira A, Bustos D.
Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria. International Journal of Molecular Sciences. 2024; 25(8):4303.
https://doi.org/10.3390/ijms25084303
Chicago/Turabian Style
Morales, Natalia, Elizabeth Valdés-Muñoz, Jaime González, Paulina Valenzuela-Hormazábal, Jonathan M. Palma, Christian Galarza, Ángel Catagua-González, Osvaldo Yáñez, Alfredo Pereira, and Daniel Bustos.
2024. "Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria" International Journal of Molecular Sciences 25, no. 8: 4303.
https://doi.org/10.3390/ijms25084303
APA Style
Morales, N., Valdés-Muñoz, E., González, J., Valenzuela-Hormazábal, P., Palma, J. M., Galarza, C., Catagua-González, Á., Yáñez, O., Pereira, A., & Bustos, D.
(2024). Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria. International Journal of Molecular Sciences, 25(8), 4303.
https://doi.org/10.3390/ijms25084303
