Computational methods help researchers organise possibilities, compare candidates, test assumptions, and decide what to investigate next. Their value depends on the question, the representation of the system, the controls, and the care used in interpretation.
Prediction is not observation
A docking pose is a model of a possible interaction under a chosen method. A similarity score is a comparison under a chosen representation. A machine-learning output reflects its training data, target definition, and evaluation design.
None of these is direct experimental proof of binding, activity, safety, or clinical efficacy.
The pipeline shapes the result
Structure preparation, protonation, tautomer handling, conformer generation, missing residues, search space, scoring functions, and parameter choices can all affect a computational result.
A reproducible workflow should record these decisions rather than presenting the final table as if it appeared without intervention.
Controls give a result context
Where appropriate, known references, decoys, alternate methods, sensitivity checks, or comparisons with existing evidence can show how a workflow behaves. Controls do not remove uncertainty; they make performance and failure modes easier to examine.
Use conclusions that match the evidence
Prefer language such as “prioritised for follow-up under this workflow” over “proven active.” Separate computational ranking from biological interpretation and clearly state the experimental or analytical work required next.
Working checklist
Before you move forward.
- Question and intended decision stated
- Input provenance and preparation recorded
- Method and parameters documented
- Controls or comparisons considered
- Uncertainty and failure modes reported
- Experimental validation not implied when absent