A molecular model can contain a lot of information, but a poor color choice can make that information harder to read. A continuous gradient used for unrelated categories may suggest an order that is not there; a collection of similar shades can make separate chains or regions difficult to tell apart. Choosing a palette by what it communicates—not just by what looks attractive—is a useful first step toward clearer molecular visuals.
SAMSON’s default palettes are grouped by type. That organization offers a practical way to start: decide what kind of information you want color to represent, then choose a palette family that suits it. The categories below are a guide to making that choice, rather than a ranking of palettes.
First ask: what does color mean here?
Use discrete palettes for distinct groups. If each color stands for a separate item or category—such as different chains, components, or classes—you generally want colors that can be distinguished from one another, not a gradient that implies a low-to-high scale. The discrete collection includes Accent, Carto Safe, Okabe-Ito, Set1, tab10, and others. Try a few against your structure: the best option is the one that keeps the groups easy to distinguish at the scale and background you plan to use.
Use a sequential palette for values that progress in one direction. A sequential color scheme is a natural fit when the data run from lower to higher values and there is no meaningful central point. The available sequential HCL palettes include Blue, Green, Heat, Inferno, Plasma, Viridis, and several others. For a first comparison, here is Viridis:
Use a diverging palette when the midpoint matters. If values make sense as departures in two directions from a meaningful center—such as below versus above a reference value—a diverging scheme can make the two sides visible. SAMSON lists Berlin and Blue-Red palettes in its diverging HCL group. Flexible diverging palettes include options such as RdBu, PiYG, Spectral, and pLDDT. The names identify available palettes; choose one based on how it renders your data and whether the color transitions support the distinction you need.
A quick workflow for a more readable view
- Write down the meaning of the colors. For example: “each color identifies a separate chain” or “color indicates a value from low to high.” If that sentence is unclear, the palette choice may be premature.
- Match the palette family to the data. Start with discrete for separate groups, sequential for one-way progression, and diverging when a central reference separates two sides. The documentation also lists qualitative HCL palettes, including Default, Pastel, Warm, and Cold, as well as flexible diverging palettes.
- Check the view in context. A palette preview is useful, but the structure itself is the real test. Look for groups that blend together, details that disappear, or colors that distract from the feature you want people to notice.
- Check direction and interpretation. SAMSON’s color palette or color scheme dialogs let you revert the left and right arms of palettes. This is useful when the direction of a gradient or the assignment of its ends does not match your intended reading. Make sure any labels or explanations still agree with the colors.
The palette names are a starting point, not a substitute for explaining the visualization. If someone else will inspect the model, add a concise key or caption describing what the colors represent. That small bit of context can prevent viewers from mistaking group identity for a measured quantity—or a gradient for a categorical distinction.
SAMSON uses HCL palettes; HCL stands for Hue-Chroma-Luminance. If the defaults do not fit your use case, the documentation also explains how to create custom HCL palettes. You can also explore Colorizing and Visual presets for related workflows.
For the complete lists of default palettes, see the SAMSON color palettes reference.
Note: SAMSON and all SAMSON Extensions are free for non-commercial use. Get SAMSON.