Graph Learning for Drug Discovery with Cellular Context

Abstract

This scholarly review examines cellular context in graph-guided drug discovery and biomedical evidence translation. It connects evidence on graph neural networks and reinforcement learning for drug discovery, cyclin f expression and liver-cancer prognosis, il-1β-stimulated synovial mesenchymal stem-cell bioinformatics through a layered account of observation, representation, decision, and deployment. The cited studies are not pooled, and the article introduces no new experiments, datasets, clinical findings, or performance estimates. Instead, it asks which assumptions must remain visible as information moves from a source study into an operational model. The analysis distinguishes semantic validity from predictive accuracy, identifies interfaces at which provenance can be lost, and proposes review gates for evaluation under distribution shift. The synthesis suggests that robust systems require traceable evidence objects, domain-specific error taxonomies, calibrated human interpretation, and explicit escalation rules. These principles support method transfer without collapsing distinct physical, biological, clinical, or computational settings into a single empirical claim.