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Self assembly of viral biopolymers to nano‐complexes forming virions during virus delivery from infected cell and reverse disintegration to virus entry into new cells play a crucial role in viral life cycle and in viral diseases. Therefore artificial instruments for selective counter intervention into these processes are dramatically required for the high effective antiviral protection. Hybrid macromolecular systems (HMS) rationally integrating heterogeneous structure‐functional factors for selective recognition ‐ inhibition of viruses (nano‐objects) without detriment for cells (micro‐objects) can become a molecular basis for cardinal progress in this area. Here we discuss approaches to design and current experimental results of synthesis, and antiviral selectivity evaluations of the HMS, based on combinations of polyelectrolyte‐grafted components constructed on principles of mimicry and/or complementarity to viral targets or virus‐sensitive cell receptors. Particularly, the HMS generations strongly inhibiting the human immunodeficiency virus (HIV) were created as platform to novel drug development against HIV/AIDS and other sexually transmitted infections.

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Let n ∈ N and X(n) = (X 1 (n), . . . , X d(n) (n)) be a sequence of random vectors. We prove that, under certain dependence conditions, the cdf of the maximum of X i (n) asymptotically equals to the cdf of the maximum of a random vector with the same but independent marginal distributions. To prove our result on extremal independence, we obtain new lower and upper bounds on the probability that none of a given finite set of events occurs. Using our result, we show that, under certain conditions, including Berman-type condition, a sequence of Gaussian random vectors possesses the extremal independence property. We also prove that certain extremal characterstics of binomial random graphs and hypergraphs, after an appropriate rescaling, have asymptotical Gumbel distribution (in particular, maximum codegrees in random hypergraphs and the maximum number of cliques sharing a given vertex in random graphs).