We developed the pipeline to leverage the power of the variational autoencoder and its latent spaces for the design of promising biocatalysts (Fig. ). This pipeline was inspired by the previous studies reporting the connections between latent space geometry and phylogeny for a given MSA . Our main motivation for the current study was to exploit this connection to generate new protein sequences, the direction that was not explored in the previous studies. We iteratively executed our pipeline across three rounds, each iteration followed by experimental validation to improve our workflow (Appendix section 1). In both the first and second rounds, we utilized Model 1, with the only difference being a revised selection of VAE ancestors. In the third round, we introduced variations to the MSA preprocessing and additionally manual curation of the generated ancestors by AlphaFold and MutCompute . In addition, during the third round of experimental validation, we explored the possibility of conditioning the VAE on solubility scores returned by the ML-based tool SoluProt . In total, three trained VAE models were explored in the third round, referred to as Model 2, Model 3, and Model 4, to better understand the strengths and weaknesses and refine our approach.