Author: OneAngstrom

For many molecular modelers, the first step in gaining insight from a structure is being able to see it clearly. But visualizing complex biomolecular systems can be time-consuming. Choosing how to represent atoms, bonds, chains, ligands, and setting rendering parameters…

When working with coarse-grained (CG) molecular simulations, many modelers run into unexpected behavior during solvation—particularly with Martini models. This blog post dives into why solvent setup in the GROMACS Wizard in SAMSON requires an extra step, and how skipping it…

Understanding how a ligand exits a protein’s binding pocket is critical in molecular modeling and drug discovery. Researchers often simulate multiple unbinding pathways, but visualizing and comparing them in a clear and interpretable way can be challenging. A clean visual…

One common frustration in molecular simulations is starting a production run only to realize later that the system wasn’t properly equilibrated. This often leads to inaccurate results, wasted computing time, or simulations that eventually crash. A frequent culprit? Skipping or…

Tracking specific atoms during a molecular simulation is essential when studying dynamic systems, such as interactions in flexible biomolecules or complex molecular assemblies. But how can you keep the camera focused on the part of the system that matters—without manually…

Molecular modelers working with GROMACS know the importance—and the complexity—of setting the right molecular dynamics parameters. Whether it’s energy minimization, NVT/NPT equilibration, or production simulations, configuring .mdp (molecular dynamics parameter) files can become a time-consuming part of the workflow, especially…

Creating Python scripts to manipulate molecular models can be time-consuming, especially if you’re learning the SAMSON API or trying to prototype quickly. Fortunately, SAMSON AI introduces a useful shortcut: the /script command. This feature allows SAMSON AI to generate Python…

For many molecular modelers working on nanotechnology, designing carbon nanotubes (CNTs) is a frequent need—but doing it manually can be tedious and error-prone. Whether you’re trying to create a specific chirality or simulate functionalized tube ends, having fine control over…