Many molecular modeling tasks are not difficult because of a single complex step. The friction comes from repeating the same sequence of actions: preparing a model, running a familiar operation, checking the result, and doing it again for another system. Repetition takes time, and it can make it harder to keep a workflow consistent.
SAMSON’s scripting and extension paths offer two different ways to reduce that friction. The useful first question is not “What should I automate?” but “Which part of my work do I repeat, and what kind of change would help?” That distinction can point you to the right documentation without sending you down an unnecessary path.
Start with the task, not the tool
If you want to automate work inside SAMSON, begin with Scripting. The scripting guide covers the integrated Python environment, package management, running scripts, and embedding scripts or apps in documents. These are relevant when your goal is to make a repeated procedure easier to run or to keep a scripted workflow with a document.
Before writing anything, describe the recurring task in plain language. What do you start with? What actions do you perform in the same order? What result tells you the task is complete? This simple outline helps separate repeatable steps from decisions that still need your judgment. It also gives you a manageable starting point: automate one stable, well-defined part rather than trying to capture an entire modeling process at once.
Think about reuse and reproducibility
A script is most useful when you can understand when and why to run it. Keep a short explanation of its purpose and the assumptions it depends on. If a workflow is intended for a particular document or for sharing with collaborators, consider whether embedding a script or app in the document fits your goal. The documentation identifies document-embedded Python workflows as one of the uses covered by Scripting.
It helps to distinguish “repeatable” from “automatic.” A script can reduce repeated manual work, but you still need to decide whether its inputs and assumptions suit the current model. Treat the result as part of your scientific workflow: check that the operation has done what you intended before relying on it in later analysis. The benefit is not that every modeling decision disappears; it is that routine steps can be made more consistent and easier to revisit.
Know when an extension is the better route
Sometimes the problem is not a repeated sequence of actions. You may need a capability that is not currently available in your setup, or you may want to understand how additional capabilities are installed, developed, or distributed. In that case, go to Extending SAMSON. It covers extension management and introduces the SDK and extension development path.
You do not need to study extension development just to automate a task with Python. Likewise, if you only need built-in capabilities and are not managing extensions, the extension path can wait. Choosing the smaller, relevant path saves time: scripting for automation and document-based Python workflows; extending SAMSON for adding capabilities or exploring development and distribution.
A practical starting point
- Pick one recurring task that has clear, repeatable steps.
- Write down its inputs, sequence, and expected outcome.
- Use the Scripting guide to explore the integrated Python environment and running scripts.
- Decide whether keeping the script or app in a document would help you reuse or share the workflow.
- If your goal is a new capability rather than task automation, consult Extending SAMSON instead.
This approach keeps automation grounded in the real work of molecular modeling: fewer repeated manual steps, a clearer account of how a procedure is run, and a sensible route to further capabilities when you need them. For the complete overview and links to related topics, see the Automate and extend documentation.
Note: SAMSON and all SAMSON Extensions are free for non-commercial use. Get SAMSON.