Discovery of Bioresorbable Polymer Suture Coatings for Controlled Tissue Regeneration with Multimodal Foundation Models

Therapeutic sutures can localize bioactive payloads to wounds, but burst release and insufficient coating durability can compromise regeneration and increase infection risk. In this work, we introduce a data-driven discovery strategy for bioresorbable polymer suture coatings that improve therapeutic retention and release kinetics. We develop $\textbf{GenPoly}$, an end-to-end multimodal generative framework that learns a shared polymer representation over paired polymer string sequences and molecular graphs via contrastive alignment, and couples this representation to property prediction, synthetic accessibility filtering, and motif-based candidate clustering to discover coating families beyond commonly used linear aliphatic polyesters. Starting from a clinically prevalent baseline, GenPoly identifies candidate coatings and selects representative polymers for experimental evaluation on nanoparticle-loaded sutures with recombinant human EGF to an in vitro human keratinocyte wound model. A discovered polymer coating of the LA-TMC family shows improved payload retention and sustained release, exhibiting a $3.3\times$ increase in rhEGF concentration, accompanied by more localized Ki-67–positive keratinocyte proliferation at the wound bed, in comparison with the current state-of-the-art candidates. These findings suggest new opportunities in AI-driven polymer discovery, and highlight the viability of GenPoly in engineering bioresorbable coatings for suture-based therapeutic delivery in targeted tissue regeneration.