Aug 2026· Journal of Chemical Information and Modeling· 0 citations· 62 references
TL;DR
A new software package, Fast-Forward, which aids parametrization of models for the widely used Martini coarse-grained force field, and offers a system-agnostic suite of tools to parametrize molecules of any size, existing in any environment.
Abstract
Coarse-grained molecular dynamics simulations of bio- and macromolecular systems offer a method of accessing otherwise unobtainable time and length scales, compared to atomistic simulation techniques. However, a limiting step is often the generation and validation of the coarse-grained models. Here, we describe a new software package, Fast-Forward, which aids parametrization of models for the widely used Martini coarse-grained force field. In comparison to other similar packages, Fast-Forward offers a system-agnostic suite of tools to parametrize molecules of any size, existing in any environment. It achieves this while maintaining ease of use and the use of interoperable file formats from the Martini software ecosystem. Through its three subprograms, the package offers tools for trajectory mapping, parameter generation, and model validation. We demonstrate the potential of the package for several different use cases, from small biological molecules (glutathione and glutathione disulfide) to models of synthetic polymers (poly(methyl methacrylate), PMMA).
A fast pipeline based on the OPLS-AA force field is presented that enables the automated calculation of non-bonding intermolecular (dimer) interaction energies derived from a set of small organic (monomer) molecules. To calculate the non-bonding contributions, optimized geometries of the monomer molecules as well as their OPLS-AA van der Waals and atomic partial charge parameters are required. The key advantage of the new pipeline lies in its fast, highly parallelized in-memory computations without slow I/O operations, which make it possible to thoroughly process comparatively large numbers of (more than a hundred) monomer molecules within acceptable time frames (hours and days): Compared to an analogous, more versatile, and comprehensive approach, the new computational scheme delivers comparable results while being more than two orders of magnitude faster. For locally estimating the required OPLS-AA force field parameters with the LigParGen and BOSS software packages, a user-friendly graphical user interface for the Windows operating system is provided. Scientific contribution Rapid calculation of mutual intermolecular energies for a set of monomer molecules based on the widely used OPLS-AA force field enables a considerable expansion of this type of calculation for practical purposes. The performance improvement can be used to achieve significant improvements of interaction energy averages as well as considerable expansions in the size of the monomer molecule set.
Mirco Daniel, Hannah Kullik, Martin Urban et al.· Journal of Cheminformatics· 0 citations
The present work revisits the methods within CP2K that turn electronic structure into dynamics, transport, and spectroscopic response, highlighting CP2K's unique capability to unify quantum chemistry with quantum and statistical mechanics within a versatile, holistic simulation environment.
Jan Wilhelm, Anna-Sophia Hehn, Hossam Elgabarty et al.· 1 citation· ⚡1
Molecular dynamics (MD) simulations of slow biomolecular processes, such as exploration of the conformational ensembles of intrinsically disordered proteins (IDPs), are computationally demanding. Although coarse-grained (CG) models can substantially speed up the simulations compared to all-atom MD, the sampling challenge can still be significant for large systems and long time scales. Here, we present light Martini water, a low-viscosity water model that accelerates sampling in MD simulations with the Martini CG force field. We systematically reduced the mass of the Martini water beads and verified stable, accurate integration of the equations of motion with 20 fs time steps, as typically used in Martini simulations. Light Martini water has a reduced mass of 20 amu (compared to 72 amu in the standard water model), yielding up to a 2.68-fold increase in the sampling rate of IDP chain reconfiguration in water and a 16 % increase in the lateral diffusion of lipids in a POPC bilayer. Equilibrium properties remained unaffected by the mass scaling, and the speedup was achieved without compromising simulation accuracy. The water model is trivial to implement, has no computational overhead, and should be universally applicable to Martini simulations.
Aladdin Elgendy, André P. Zeipelt, Lars V. Schäfer· bioRxiv· 0 citations
The Pharmacon toolkit provides a streamlined, automated workflow that simplifies the routine analysis and postprocessing of MD simulation results of biomacromolecular complexes, making them more accessible and reproducible by aggregating many tedious and time-consuming tasks into a single command-line workflow.
Kyriakos Georgiou, A. Kolocouris· Journal of Chemical Informat...· 0 citations
Coarse-grained (CG) molecular dynamics extends polymer simulation beyond the scales accessible to all-atom (AA) methods, but bottom-up CG modeling is laborious. The CG resolution is a design choice, so a transferable parameter set is generally not available and the potentials are derived anew for each polymer mapping. Here we present CGMas, a multi-agent framework that automates topology construction, equilibration, mapping, potential derivation, and validation from a natural-language specification of the polymer and target resolution. A large-language-model (LLM) reasoning agent infers the AA topology from polymer name, while layered self-correction resolves physical errors common to unsaturated, heteroatom-containing, and polar polymers. Downstream agents equilibrate the system, map it onto CG representation, derive potentials through Boltzmann inversion, and benchmark the model against its atomistic reference. CGMas completed all 27 homopolymer and copolymer tasks, matched the AA density to within 5% in 22, and reduced simulation from 38-88 min to 1 min, establishing agentic LLMs as a route to automated polymer coarse-graining.
