Bleeding disorders arising from dysfunctional platelet–protein interactions pose a significant clinical challenge due to their heterogeneity and complexity. Primary hemostasis is mediated by von Willebrand factor (VWF) and platelet surface receptors GPIbα and αIIbβ3. This protein triad is central to clot formation and interfering with their associated activity can cause several primary hemostasis related disorders. While traditional therapies, including factor replacement and monoclonal antibodies have improved outcomes, they are often limited by availability, cost, immunogenicity, and inadequate precision. Recent advances in computational biology and peptide engineering now offer potential for improved hematologic therapeutics. This review outlines two major strategies in peptide drug design: structure-based modeling and small motif (SM)-based design. These approaches enable the creation of short, stable peptides capable of targeting disease-specific protein–protein interactions (PPIs) with high specificity. We highlight the recent development of G14 – an artificial intelligence (AI)-designed peptide that selectively disrupts the aberrant GPIbα–VWF interaction in PT-VWD. The peptide demonstrated selective inhibition of the enhanced patient-derived platelet aggregation and VWF binding. By combining systems biology, structural modeling, and AI, peptide design can now yield rapid, scalable, and personalized therapies for bleeding disorders. Thus, the growing adoption and integration of intelligently designed peptides offers a new perspective on precision medicine for thrombosis and hemostasis.
Kazmirchuk et al. (2026) studied this question.