Another promising directions for further improvement include developing better scoring methods for the sequences generated by protein language models, particularly those that will allow filtering out misfolded or poorly soluble designs in silico, and adopting the recent developments in transformerbased architectures, which have demonstrated a better capacity for learning from amino acid sequences . Integrating transformer-based architectures with manifold learning can further enhance their ability to generate sequences of stable and soluble proteins . To bolster the robustness of future studies, adopting a generation protocol for ancestral sequences that incorporates an ensemble of models might also be advantageous . This approach addresses the observed instability of ancestral trajectories within the latent space and could establish a more reliable foundation for future investigations.