The Next Fifty Years: Drug Discovery in the Age of the Computational Self

Résumé fourni par la source

Abstract For most of biomedical history, drug discovery has been organized around populations: diseases defined through shared symptoms, therapeutics evaluated in statistical cohorts, approval granted for averaged responses. The paradigm produced extraordinary successes. It also embedded a foundational assumption now under pressure from several independent directions at once. AI-native drug discovery, multi-scale digital twins, pharmacogenomics, patient-derived organoids, adaptive trial design, closed-loop biosensing, decentralized science — these are not isolated innovations. They are convergent expressions of a single directional tendency: the progressive migration of therapeutic authority from population-scale structures toward the individual. This is the biomedical expression of a broader civilizational vector, in which the organizing unit of decision-making resolves continuously toward smaller, more personalized scales. Twelve convergent indicators of this transition are introduced, along with a fifty-year roadmap projecting five successive phases of drug discovery, from AI-assisted precision medicine through full therapeutic sovereignty. The Asymmetric Epistemic Burden (AEB) model formalizes the growing divergence between AI-accelerated hypothesis generation and the bottlenecked experimental and regulatory validation that now constitutes the primary constraint on progress. The defining transformation of twenty-first century medicine may not be the discovery of a better drug class. It may be the conceptual and institutional replacement of the average patient as the fundamental unit of therapeutic reasoning — and the emergence of a pharmacotherapy organized not around populations, but around continuously modeled, computationally instantiated, biologically sovereign individuals.

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