Approximation of Collective Variables by anncolvar

Dalibor Trapl1*, Izabela Horvacanin2*, Vaclav Mareska1*, Furkan Ozcelik3*, Gozde Unal3*, Vojtech Spiwok1*

1 University of Chemistry and Technology, Prague, Czech Republik

2 Faculty of Science, University of Zagreb, Croatia

3 Istanbul Technical University, Turkey

* Corresponding authors emails: , , , , ,
DOI10.24435/materialscloud:2019.0014/v1 [version v1]

Publication date: Apr 23, 2019

How to cite this record

Dalibor Trapl, Izabela Horvacanin, Vaclav Mareska, Furkan Ozcelik, Gozde Unal, Vojtech Spiwok, Approximation of Collective Variables by anncolvar, Materials Cloud Archive 2019.0014/v1 (2019), doi: 10.24435/materialscloud:2019.0014/v1.


Biomolecular simulations are computationally expensive. This limits their application in drug or protein design and related fields. Several methods have been developed to address this problem. These methods often use an artificial force or potential acting on selected degrees of freedom known as collective variables. This requires explicit calculation of a collective variable (and its derivatives) from molecular structure. For collective variables that cannot be calculated explicitly or such calculations is slow we developed anncolvar package ( This package approximates collective variables using artificial neural networks. It was tested on Isomap low dimensional representation of cyclooctane derivative or solvent-accessible surface area of Trp-cage miniprotein.

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File name Size Description
14.1 MiB Input files (input structure, topology, Plumed input) for simulations used to demonstrate functionality of anncolvar ( Input files for metadynamics simulation of cyclooctane derivative in vacuum with three Isomap CVs, metadynamics (mtd), parallel tempering (ptmd) and parallel tempering metadynamics (ptmtd) of Trp-cage in water with solvent-accessible surface area and alpha-RMSD CVs are provided. Tested on OpenMPI4.0.0, Gromacs 2018.5 and Plumed2.5.0. Scripts provided.
4.2 KiB README.txt file with descriptions.


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.

External references

Journal reference (Paper in which we published anncolvar package ( for approximation of collective variables by artificial neural networks for metadynamics and related methods)
D. Trapl, I. Horvacanin, V. Mareska, F. Ozcelik, G. Unal, V. Spiwok, Frontiers in Molecular Biosciences 6, 25. doi:10.3389/fmolb.2019.00025


metadynamics artificial neural networks collective variable

Version history:

2019.0014/v1 (version v1) [This version] Apr 23, 2019 DOI10.24435/materialscloud:2019.0014/v1