Graph Learning from Chemical Bonds to Therapeutic Systems: A Scoping Review of Pharmaceutical Representations and Reasoning Tasks Download PDF

Graph learning has become an important computational strategy for representing chemical structures, molecular interactions, biological networks, and heterogeneous therapeutic knowledge. However, the pharmaceutical meaning of a graph model depends not only on its architecture or predictive performance but also on what its nodes and relations encode, which reasoning task is attempted, how evaluation is designed, and whether evidence extends beyond retrospective benchmarks. This scoping review maps graph representations and learning tasks across molecular, biomolecular, network, disease, and knowledge-system scales. A transparent eligibility and evidence-charting framework was used to distinguish representation choices, prediction levels, reasoning claims, validation settings, uncertainty practices, and translational boundaries. The synthesis indicates that molecular property prediction and relation prediction constitute the most methodologically developed areas, whereas pathway reasoning, cross-scale transfer, evidence-linked explanation, and decision-context evaluation remain less mature. Increasing graph breadth can expand the scope of computable relationships, but it also introduces semantic heterogeneity, provenance problems, missing relations, confounding, and greater validation requirements. The review therefore proposes a multiaxial interpretation organized by graph scale, pharmaceutical task, and evidence maturity. This framework is intended as a review-coding and reasoning device rather than a validated readiness scale. It separates benchmark capability from generalization, experimental confirmation, pharmaceutical usefulness, and routine implementation. The principal implication is that graph learning should be evaluated as a representation-dependent and claim-specific scientific instrument. Stronger therapeutic relevance will require realistic data partitions, explicit applicability domains, external and temporal validation, calibrated uncertainty, experimentally testable hypotheses, and evaluation within defined pharmaceutical decision contexts.