Information-Guided Selectivity in Viral Host-Cell Targeting: A Functional Account of Apparent Viral Purpose

Viral host-cell targeting is often described in intentional language—viruses "seek," "choose," or "target" particular cells. This paper asks what, if anything, licenses such language, and answers it without appeal to consciousness, literal purpose, or self-replication as a criterion of life. We argue that the defensible empirical core is information-guided selectivity: productive viral entry is statistically distinguishable from passive, availability-weighted encounter, and this departure can be measured in bits using standard information theory and binding thermodynamics. Parameterizing this measure from published affinity data for two of the best-characterized viral targeting systems—SARS-CoV-2 spike binding across ACE2 host orthologs, and influenza hemagglutinin binding α2,6 versus α2,3 sialosides—and propagating the reported affinity ranges by Monte Carlo, we obtain robustly positive selectivities of 1.42 bits (95% CI [1.06, 1.95]) and 0.31 bits (95% CI [0.19, 0.45]) respectively, with realized engagement concentrated overwhelmingly on the preferred targets. The measure is identically zero under a non-selective null, giving a sharp, falsifiable operationalization of "non-random targeting." We further compute the integrated information of a deliberately minimal capsid–receptor state-transition motif using the whole-minus-sum effective-information measure, obtaining a modest 0.75 bits; this exceeds every member of a matched random-network null ensemble (mean 0.32 ± 0.06 bits), yet behaves non-monotonically under lesion, and we report both facts honestly rather than treating Φ as a fitness proxy. Embedding a bounded selectivity term in a standard Susceptible–Infected–Recovered (SIR) model raises the simulated epidemic peak by 40% under an illustrative coupling; a reproducible multi-method global sensitivity analysis (Latin-hypercube/PRCC and variance-based Sobol indices) quantifies each parameter's contribution and shows selectivity to be a dependable but secondary driver. Throughout, "agency" and "intentionality" are used strictly as functional shorthand, with their metaphysical commitments explicitly bounded. All numerical results, tables, and figures are produced by a single self-contained, seeded Python script reproduced in the appendix.