Molecular Design Learned from the Natural Product Porphyra-334: Molecular Generation via Chemical Variational Autoencoder versus Database Mining via Similarity Search, A Comparative Study

A comparative study
is presented. The method via chemical variational
autoencoder (VAE) and the method via similarity search are compared,
focusing on their generation ability for new functional molecular
design. Focusing on the natural porphyra-334 as a model molecule,
we generated three groups: molecules of mycosporine-like amino acids
(MAAs) as seeds (GSEEDS), molecules
generated via chemical VAE (GVAE) and molecules gathered via similarity search (GSIM). The number of molecules that satisfy the
condition for the light absorption ability of porphyra-334 in GSEEDS, GVAE, and GSIM are 52, 138,
and 6, respectively. The method via chemical VAE shows a promising
potential for future molecular design. By using quantum chemistry
wave function properties for chemical VAE, we find new molecules that
are comparable to porphyra-334, including some with unexpected geometries.
At the end, we show a group of molecules found with this method.