A team demonstrated how integrating interpretability into AI‑driven protein engineering workflows accelerates design cycles, enabling prioritized mutations with improved functional likelihood. The approach blends data‑driven models with mechanistic constraints to reduce experimental burden during lead optimization. In computational structure prediction, sparse denoising models were introduced to generate protein backbone ensembles and accelerate in silico folding. The method produces realistic structural candidates at scale and can feed downstream design loops, potentially shortening timelines for biotherapeutic engineering and de novo protein discovery.