From Fragmentation to Convergence Computational Biology, Research Clusters, and the Future of Pharmacology in the Age of the Computational Self

Pharmacology's binding constraint is no longer biological or computational capacity. It is architecture. Bibliometric mapping of the scientific literature shows biology and computer science behaving as two largely separate continents, connected by a narrow set of bridge zones where most consequential innovation concentrates. This review maps those bridges: the cluster-level evidence for where biology and computation actually meet; the 2025–2026 wave of AI systems entering mathematics, physics, chemistry, and biology as working research partners; the reshaping of target identification, structure prediction, network pharmacology, and multi-omics integration inside drug discovery specifically; and the emerging digital-twin infrastructure that is beginning to displace the population-average patient as pharmacology's basic unit of reasoning. It then turns to the structural cost of that acceleration: an Asymmetric Epistemic Burden in which generation has become cheap while evaluation remains slow, expensive, and human-bottlenecked, and argues that the decisive advantage of the next decade belongs to whoever closes that gap rather than whoever widens it further. A fifty-year outlook closes the review, tracing the field's likely path from AI-assisted precision medicine toward what I have called Therapeutic Sovereignty.