Anncolvar: Approximation of Complex Collective Variables by Artificial Neural Networks for Analysis and Biasing of Molecular Simulations


Trapl D., Horvacanin I., Mareska V., Ozcelik F., Ünal G. , Spiwok V.

FRONTIERS IN MOLECULAR BIOSCIENCES, cilt.6, 2019 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 6
  • Basım Tarihi: 2019
  • Doi Numarası: 10.3389/fmolb.2019.00025
  • Dergi Adı: FRONTIERS IN MOLECULAR BIOSCIENCES

Özet

The state of a molecular system can be described in terms of collective variables. These low-dimensional descriptors of molecular structure can be used to monitor the state of the simulation, to calculate free energy profiles or to accelerate rare events by a bias potential or a bias force. Frequent calculation of some complex collective variables may slow down the simulation or analysis of trajectories. Moreover, many collective variables cannot be explicitly calculated for newly sampled structures. In order to address this problem, we developed a new package called anncolvar. This package makes it possible to build and train an artificial neural network model that approximates a collective variable. It can be used to generate an input for the open-source enhanced sampling simulation PLUMED package, so the collective variable can be monitored and biased by methods available in this program. The computational efficiency and the accuracy of anncolvar are demonstrated on selected molecular systems (cyclooctane derivative, Trp-cage miniprotein) and selected collective variables (Isomap, molecular surface area).