Jul 2026· Journal of Chemical Information and Modeling· Vol 66, pp. 10141 - 10155· 0 citations· 67 references
Medicine
TL;DR
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.
Abstract
We present the Pharmacon toolkit, a pure Python command-line software package for analyzing molecular dynamics (MD) simulations of biomacromolecules. Pharmacon supports trajectories generated by widely used MD simulation engines, such as Amber, Gromacs, CHARMM, NAMD, and OpenMM. Pharmacon is written entirely in Python and uses popular third-party libraries, including MDAnalysis for trajectory handling and NumPy for data processing. The 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. As a proof of concept, we applied Pharmacon to analyze, in comparison with other toolkits, results from MD simulations, including intermolecular interactions and geometric measures, across different test protein complexes, including three membrane proteins and one soluble protein complex.
Molecular dynamics (MD) simulations have become an increasingly important component of modern medicinal chemistry and structure-based drug discovery, providing atomistic insight into protein-ligand interactions that extends beyond static experimental structures and docking models. By explicitly accounting for conformational flexibility, solvent effects, and time-dependent behaviour, MD simulations enable the refinement of binding poses, the identification of transient and allosteric sites, and the quantitative estimation of binding thermodynamics and kinetics, the latter increasingly accessible through Markov state models (MSMs) and milestoning approaches that reconstruct long-timescale behaviour from ensembles of short trajectories. In this mini-review, we provide a practical overview of classical atomistic MD methodologies commonly used in medicinal chemistry, including force-field-based simulations, enhanced sampling techniques, and free-energy calculation methods such as alchemical and end-point approaches. Emphasis is placed on the strengths and limitations of each technique, with particular attention to their appropriate use across different stages of the drug discovery pipeline. We further discuss best practices for system preparation, simulation protocol design, convergence assessment, and reproducibility, highlighting common pitfalls that can lead to overinterpretation of simulation results. Selected examples illustrate how MD simulations have informed medicinal chemistry decisions in lead identification and optimisation. Finally, we briefly outline emerging directions, including the integration of machine learning, ensemble-based approaches, and next-generation force fields, which are expected to further expand the role of MD simulations in medicinal chemistry.
S. S. Çınaroğlu· Mini-Reviews in Medical Chem...· 0 citations
Classical molecular dynamics is a theoretical method useful for investigating noncovalent intermolecular interactions and conformational flexibility, enabling the simulation of processes such as aggregation/dissolution and adsorption. Molecular dynamics and quantum chemistry are increasingly introduced in chemistry curricula, yet students may encounter difficulties when translating theoretical concepts into robust simulation protocols, particularly outside biomolecular systems. Here, we present a tutorial designed to guide students through the preparation, execution, and analysis of the simulations of small organic molecules in nonaqueous solvents, followed by the calculation of ultraviolet–visible (UV–vis) absorption spectra using time-dependent density functional theory. This tutorial focuses on several practical issues: obtaining force-field parameters, building simulation cells, selecting ensembles, and interpreting critical simulation parameters. Custom solvent models are validated against reference density and radial distribution functions, demonstrating how selected observables can be used to assess simulation reliability. We illustrate the usefulness of molecular dynamics by showing that molecular flexibility and solvent interactions broaden the UV–vis absorption band of indigo in chloroform. This contribution is intended as a hands-on educational resource for upper-level undergraduate and graduate students enrolled in physical chemistry and computational chemistry courses, as well as for Ph.D. students working in chemistry, physics, or material science, who are approaching molecular simulations. To support dissemination, we have released a ready-to-use package on GitHub that includes solvent boxes and all the necessary files and data for incorporating new molecules into GROMACS simulations: https://github.com/cippee/SolventSafari-for-GROMACS.
Unknown authors· Journal of Chemical Educatio...· 0 citations
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.
Christopher Brasnett, Maximilian Fidlin, Thilo Duve et al.· Journal of Chemical Informat...· 0 citations
Gaussian accelerated molecular dynamics (GaMD) enhances conformational sampling by adding a smooth boost potential without requiring predefined collective variables, but an engine-integrated implementation has not been available in GROMACS. Here, we implement total-, dihedral-, and dual-boost GaMD in GROMACS 2025.4, including staged energy-statistics collection, GPU-based bias evaluation and force scaling, restart support, and outputs required for cumulant-based free-energy reweighting. The implementation was evaluated using four benchmark systems spanning conformational free energies, protein folding, and ligand recognition. For alanine dipeptide, a reweighted 100 ns GaMD trajectory recovered the major free-energy basins and rotational barriers in overall agreement with a 1000 ns conventional MD simulation. For chignolin and TC5b, all three independent trajectories for each system sampled native-like folded states from extended conformations within 300 ns and 1 μs, respectively; the best TC5b structure had a minimum backbone RMSD of 0.03 nm from the experimental structure. In the benzene–T4 lysozyme system, two of five independent 500 ns trajectories captured both ligand binding and dissociation, yielding a bound pose with a minimum ligand RMSD of 0.06 nm from the crystal structure. Across all four systems, the boost-potential distributions were approximately Gaussian, and second-order cumulant reweighting resolved the expected conformational and binding free-energy basins. These results demonstrate that GROMACS-GaMD provides a practical, GPU-enabled, collective-variable-free enhanced-sampling framework for biomolecular free-energy calculations, protein folding, and ligand-binding studies.
High-performance computing (HPC) is essential to modern molecular dynamics (MD) simulations. While computing capabilities continue to grow, it remains unclear how these advances translate into everyday scientific practice. Additional resources may be used to reduce time-to-solution, improve model fidelity, simulate larger systems, extend simulated time scales, or increase the number of independent runs. Understanding how users allocate increased computational capability is important for future system design. To investigate how MD usage has evolved over time, we analyzed over 400 publications reporting all-atom MD simulations using a large language model (LLM)-based extraction workflow. Thirty of these publications were manually reviewed to develop and validate an expert-guided extraction rubric. Using this rubric, we evaluated several LLM configurations for extracting system size, simulation duration, and number of independent runs. Extraction quality improved across successive model generations, from GPT-4o to GPT-5 and then to GPT-5.4. Within the GPT-5.4 family, accuracy decreased as model size was reduced from GPT-5.4 to GPT-5.4-mini and then to GPT-5.4-nano. However, increasing reasoning effort from medium to high made GPT-5.4-nano performance comparable to GPT-5.4 while reducing cost by nearly an order of magnitude. Based on this result, GPT-5.4-nano with high reasoning effort was selected for extraction across the full dataset. Our initial analysis suggests that MD system sizes and the number of independent runs have not grown substantially over the last two decades, whereas simulation duration shows a clearer, approximately exponential increase. The fitted trend suggests that maximum MD simulation duration doubled approximately every 2.2 years between 2005 and 2025. These results demonstrate the feasibility of AI-assisted meta-analysis for understanding historical trends in scientific computing workloads.
Alexey N. Simakov, Nikolay A. Simakov· Practice and Experience in A...· 0 citations
This Perspective surveys the central methodological challenges in developing ML/MM frameworks, including the generation of high-quality reference data and the treatment of multiscale coupling.
Xinhu Sha, Chenyu Wu, Daiqian Xie et al.· Journal of Physical Chemistr...· 0 citations
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