When working with molecular libraries, identifying subsets of molecules that contain specific functional groups can be both repetitive and error-prone. Whether you’re hunting for fluorinated compounds, analogs with nitrogen-containing backbones, or custom motifs for SAR analysis, manual filtering isn’t scalable.…
As molecular modelers, we often need specific tools for our research—whether it’s visualization tweaks, structure preparation helpers, or access to advanced simulation algorithms. But installing and managing plug-ins or modules across platforms can be frustrating and time-consuming, especially when you…
When studying molecular systems—especially large biomolecules—understanding how ligands move, how proteins shift domains, or how groups of atoms migrate through a structure is often central to your investigation. Yet, translating this dynamic behavior from raw trajectories into visually meaningful representations…
Integrative molecular modeling often benefits significantly from using Python packages—whether it’s for data science, simulation, machine learning, or visualization. But managing packages in scientific platforms can often be a frustrating task, especially when setting up environments or dealing with dependencies.…
When analyzing molecular simulations or transitions between conformations, it’s often important to observe not just how a system evolves forward in time—but also how it retraces its steps. Whether you’re investigating conformational cycles, analyzing equilibration processes, or simply making presentations…
When simulating complex molecular systems in motion—protein-ligand interactions, polymer dynamics, or viral capsid assembly—keeping track of relevant parts of the system visually can be surprisingly difficult. As molecules twist, translate, or diffuse across the simulation space, camera viewpoints tend to…
