The pipeline does not end when a candidate is tested. Measured results return to the model and to the design criteria, and the next round begins from a better-informed position. This is what makes the process a cycle rather than a one-way flow.
Two distinct things travel back along the return path. The first is data: each measured outcome — the real binding value, the real stability, the real cellular behaviour — becomes a new training example. The model that predicted the candidate's properties now has one more case where it can compare what it said with what actually happened. Over many rounds, this corrects systematic errors, particularly for chemical regions where the original training data was thin.
The second is criteria. Suppose several candidates from one round bind well but all degrade quickly in cells. That pattern is not a property of any single molecule; it is a signal that the design criteria underweighted stability. The next round adjusts the filters and the scoring weights so that stability counts for more. The model's numbers improve because the data improved, and the shortlist improves because the criteria improved.
The loop repeats: generate, score, filter, synthesize, measure, feed back. Each pass narrows the uncertainty. It does not eliminate it, because every new round introduces candidates the model has still never seen measured.