Keywords
structural
Abstract
This review examines vision-language reasoning for drug-selectivity evidence through an evidence-centered design lens. The analysis asks which representations support a reviewable selectivity claim. It treats the relevant unit as a complete pathway from data or physical observations to representation, model output, human interpretation, and accountable action. The cited literature is synthesized without inventing experiments or unreported performance values. Particular attention is given to generalization scores concealing mechanistic errors. The review argues that credible translation requires explicit evidence boundaries, uncertainty-aware evaluation, author-visible traceability, and a documented route for intervention. The resulting framework supports structure-informed drug discovery while distinguishing component promise from system readiness.
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