Property Map Collective Variable as a Useful Tool for a Force Field Correction

Abstract

The accuracy of biomolecular simulations depends on the accuracy of an empirical molecular mechanics potential known as a force field: a set of parameters and expressions to estimate the potential from atomic coordinates. Accurate parametrization of force fields for small organic molecules is a challenge due to their high diversity. One of the possible approaches is to apply a correction to the existing force fields. Here, we propose an approach to estimate the density functional theory (DFT)-derived force field correction which is calculated during the run of molecular dynamics without significantly affecting its speed. Using the formula known as a property map collective variable, we approximate the force field correction by a weighted average of this force field correction calculated only for a small series of reference structures. To validate this method, we used seven AMBER force fields, and we show how it is possible to convert one force field to behave like the other one. We also present the force field correction for the important anticancer drug Imatinib as a use case example. Our method appears to be suitable for adjusting the force field for general drug-like molecules. We provide a pipeline that generates the correction; this pipeline is available at .

Molecular Modelling of Ionic Liquids: Situations When Charge Scaling Seems Insufficient

“…Molecular simulation as a powerful tool to probe the microscopic motions of different components in complex systems has been widely applied in various physical, chemical, and biological situations [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. In atomistic simulations of ILs, fixed-charge force fields are often employed due to the cell size required to eliminate finite-size artifacts and the time scale needed to thoroughly sample the configurational space of relevance.…”

DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space

“…The implementation of the aforementioned CV methods is important and needed for ease of use in real-life enhanced sampling problems . A lot of computer software indicates that the development of this kind of widely used solution has gradually gained interest from the community and especially triggered the applicability of enhanced sampling methods. − As far as we are aware, many existing packages and libraries provide only general geometric CVs, such as bond distance, angle, rotation, and path-CV, and have still been missing the implementation of modern ML/DL-based CV algorithms. A similar situation also occurs in the case of the DAENN; therefore, the use of DAENN could be inconvenient for users who are not familiar with code modification and programming.…”