STAFF PROFILE
Dr Tu Le
Position:
VC Postdoctoral Fellow
College / Portfolio:
School of Engineering Cluster
School / Department:
Manufacturing, Materials and Mechatronics
Phone:
+61 3 9925 2216
Email:
tu.le@rmit.edu.au
Campus:
Melbourne City Campus
Contact me about:
Research supervision
- Winkler, D.,Le, T. (2017). Performance of deep and shallow neural networks, the universal approximation theorem, activity cliffs, and QSAR In: Molecular Informatics, 36, 1 - 6
- Le, T.,Tran, N.,Mulet, X.,Winkler, D. (2016). Modeling the influence of fatty acid incorporation on mesophase formation in amphiphilic therapeutic delivery systems In: Molecular pharmaceutics, 13, 996 - 1003
- Le, T.,Yin, H.,Chen, R.,Chen, Y.,Zhao, L.,Casey, P.,Chen, C.,Winkler, D. (2016). An experimental and computational approach to the development of ZnO nanoparticles that are safe by design In: Small, 12, 3568 - 3577
- Le, T.,Winkler, D. (2016). Discovery and optimization of materials using evolutionary approaches In: Chemical Reviews, 116, 6107 - 6132
- Le, T.,Yan, B.,Winkler, D. (2015). Robust prediction of personalized cell recognition from a cancer population by a dual targeting nanoparticle library In: Advanced Functional Materials, 25, 6927 - 6935
- Le, T.,Winkler, D. (2015). A bright future for evolutionary methods in drug design In: ChemMedChem: chemistry enabling drug discovery, 10, 1296 - 1300
- Le, T.,Ballard, M.,Casey, P.,Liu, M.,Winkler, D. (2015). Illuminating flash point: comprehensive prediction models In: Molecular Informatics, 34, 18 - 27
- Van 't Hag, L.,Darmanin, C.,Le, T.,Mudie, S.,Conn, C.,Drummond, C. (2014). In meso crystallization: compatibility of different lipid bicontinuous cubic mesophases with the cubic crystallization screen in aqueous solution In: Crystal Growth and Design, 14, 1771 - 1781
- Le, T.,Mulet, X.,Burden, F.,Winkler, D. (2013). Predicting the complex phase behavior of self-assembling drug delivery nanoparticles In: Molecular Pharmaceutics, 10, 1368 - 1377
- Salahinejad, M.,Le, T.,Winkler, D. (2013). Capturing the crystal: Prediction of enthalpy of sublimation, crystal lattice energy, and melting points of organic compounds In: Journal of Chemical Information and Modeling, 53, 223 - 229
Materials science, machine learning, artificial neural networks, computational chemistry,