Single-shot design of a cyclic peptide inhibitor of HIV membrane fusion.

HIV evades immune detection through rapid mutation of its surface proteins, yet essential steps in viral entry, such as CD4 and co-receptor engagement, remain highly conserved. While therapies like Lenacapavir represent major advances, the emergence of resistant strains highlights the urgent need for adaptable, rapid-response antivirals. This challenge extends beyond HIV, demanding scalable design strategies for diverse viral threats. Here, we demonstrate that AI-driven design can address this need by generating cyclic peptide binders targeting a previously unexploited interface on the HIV-1 fusion protein gp41. Using only sequence information, without prior structural or binding site data, we designed and experimentally validated a single candidate. This inhibitor potently blocked infection by two HIV-1 strains in cell-based assays with no detectable cytotoxicity. Affinity analysis with SPR confirms the interaction with gp41 as designed. Our findings illustrate how AI-guided peptide design, coupled with rapid in-vitro validation, can accelerate early-stage therapeutic discovery and enable timely intervention against emerging viral threats.