eSSENCE research in focus

Virtual Chemistry

David van der Spoel

David van der Spoel


David van der Spoel and his group at the department of Cell and Molecular Biology at Uppsala University are using large scale calculations to validate and improve available methods for predicting physicochemical properties of compounds in the gas- and liquid phases. Until now, the group has been able to identify over 20 dubious experimental data points.




Some recent publications by eSSENCE participants

J. Ahlkrona, Computational Ice Sheet Dynamics. Error Control and
Efficiency. Doctoral dissertation, Uppsala University, 2016.

A. Bille, S. Mohanty, A. Irbäck, Peptide folding in the presence of
interacting protein crowders, J. Chem. Phys. 144, 175105, 2016.

M. Große Ruse, D. Hasselquist, B. Hansson, M. Tarka and M. Sandsten,
Automated Analysis of Song Structure in Complex Bird Songs, Animal
Behaviour, 112, 39-51, 2016.

L. Larsson, Event Detection in Eye-Tracking Data for Use in Applications
with Dynamic Stimuli. Doctoral dissertation, Lund University, 2016.

O. Spjuth, E. Bongcam-Rudloff, J. Dahlberg, M. Dahlö, A. Kallio, L.
Pireddu, F. Vezzi, E. Korpelainen, Recommendations on e-Infrastructures
for next-generation sequencing, GigaScience, 5(1), 1-9, 2016.

Matti Hellström, Daniel Spångberg, and Kersti Hermansson
“Treatment of Delocalized Electron Transfer in Periodic and
Embedded Cluster DFT Calculations: The Case of Cu on
ZnO(1010)”, Journal of Computational Chemistry 36, 2394–2405 (Dec. 2015)

Marketa Kaucka, Evgeny Ivashkin, Daniel Gyllborg, Tomas Zikmund, Marketa
Tesarova, Jozef Kaiser, Meng Xie, Julian Petersen, Vassilis Pachnis,
Silvia K Nicolis , Tian Yu, Paul Sharpe, Ernest Arenas, Hjalmar Brismar,
Hans Blom, Hans Clevers , Ueli Suter, Andrei S Chagin, Kaj Fried,
Andreas Hellander and Igor Adameyko, Analysis of neural crest-derived
clones reavals novel aspects of facial development, Sci. Adv., 2 (8),
e1600060, 2016.

Jonathan Alvarsson, Samuel Lampa, Wesley Schaal, Claes Andersson, Jarl E. S. Wikberg and Ola Spjuth. Large-scale ligand-based predictive modelling using support vector machines. Journal of Cheminformatics, 8:39, 2016
DOI: 10.1186/s13321-016-0151-5