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All Outputs (3)

Machine learning insights into predicting biogas separation in metal-organic frameworks (2024)
Journal Article
Cooley, I., Boobier, S., Hirst, J. D., & Besley, E. (2024). Machine learning insights into predicting biogas separation in metal-organic frameworks. Communications Chemistry, 7(1), Article 102. https://doi.org/10.1038/s42004-024-01166-7

Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation pro... Read More about Machine learning insights into predicting biogas separation in metal-organic frameworks.

Discovery of novel SOS1 inhibitors using machine learning (2024)
Journal Article
Duo, L., Chen, Y., Liu, Q., Ma, Z., Farjudian, A., Yong Ho, W., …Tang, B. (2024). Discovery of novel SOS1 inhibitors using machine learning. RSC Medicinal Chemistry, 15(4), 1392-1403. https://doi.org/10.1039/D4MD00063C

Overactivation of the rat sarcoma virus (RAS) signaling is responsible for 30% of all human malignancies. Son of sevenless 1 (SOS1), a crucial node in the RAS signaling pathway, could modulate RAS activation, offering a promising therapeutic strategy... Read More about Discovery of novel SOS1 inhibitors using machine learning.

Applications of Quantum Chemistry in Biomimetic Syntheses of Polycyclic Furanocembrane Derivatives (2024)
Journal Article
Wang, D., Zhou, T., Ren, J., Hirst, J., Gao, Z., & Tang, B. (2024). Applications of Quantum Chemistry in Biomimetic Syntheses of Polycyclic Furanocembrane Derivatives. SYNLETT, https://doi.org/10.1055/a-2239-6577

This paper summarizes the guidance provided by quantum chemical calculations to the biomimetic syntheses of polycyclic marine furanocembrane derivatives. Polycyclic furanocembrane derivatives are a group of structurally complex and biologically impor... Read More about Applications of Quantum Chemistry in Biomimetic Syntheses of Polycyclic Furanocembrane Derivatives.