Recent advances in protein structure prediction, most notably AlphaFold, have demonstrated near-experimental accuracy for many classes of proteins. Despite this success, AlphaFold-class systems remain fundamentally stochastic, lacking formal guarantees of determinism, replayability, and auditability across time, hardware, and software environments. These limitations constrain their use as reliable computational evidence in regulated and safety-critical scientific workflows. This paper argues that predictive accuracy alone is insufficient to advance the state of the art in scientific artificial intelligence. We analyze current techniques used to mitigate nondeterminism in AlphaFold-class systems-including fixed random seeds, constrained sampling, deterministic GPU kernels, and rigid environment control-and show that these approaches enforce reproducibility procedurally rather than structurally. We then introduce a deterministic systems perspective based on the Deterministic Computation Law (DCL), which establishes canonicalized state representation, invariant decision rules, and replayable reasoning as necessary conditions for reproducible computation. We show that DCL does not modify or replace AlphaFold’s learned predictive models, but instead constrains their use within a deterministic judgment framework that enables reproducible selection, verification, and reuse of predicted structures. By separating stochastic perception from deterministic decision-making, this framework advances AlphaFold-class systems from high-accuracy predictors to reproducible, auditable, and regulator-ready computational tools, establishing determinism as an independent axis of progress in scientific AI.