Artificial intelligence is increasingly used to represent molecules, predict properties, generate candidates, infer biological states, plan syntheses, and coordinate experimental tools. These capabilities have encouraged the broader idea of a pharmaceutical world model: a computational system intended to represent relevant scientific states, anticipate how interventions alter those states over time, and support reasoning about downstream consequences. However, the evidentiary conditions under which such a system could be treated as a scientific instrument remain unresolved. This state-of-the-art review critically examines the frontier of pharmaceutical world-model architectures, representational commitments, reasoning capabilities, validation practices, failure modes, and readiness boundaries. The review uses a claim-centred analytical framework that distinguishes component capability from integrated simulation, retrospective benchmark performance from prospective experimental confirmation, association from causality, model confidence from calibrated uncertainty, and molecular plausibility from therapeutic relevance. The principal synthesis is a proposed scientific-instrument test requiring explicit representation of state, intervention, temporal transition, causal assumptions, uncertainty, applicability, and measurable biological consequences. Existing foundation, multimodal, generative, perturbational, planning, and agentic systems provide important components of this architecture, and selected prospective studies demonstrate that model outputs can contribute to experimentally testable hypotheses. Nevertheless, the available evidence does not establish a general, intervention-faithful, causally reliable, independently reproducible, and routinely qualified pharmaceutical world model. Important limitations include narrow task definitions, benchmark leakage, limited transportability, incomplete uncertainty evaluation, selective reporting of successful hypotheses, and weak linkage to downstream development decisions. Pharmaceutical world models may become scientific instruments only through intended-use qualification, prospective consequence-based testing, cross-site reproduction, transparent failure reporting, and continuing re-evaluation under distributional and workflow change.