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

Yuki Harada, Makoto Hatakeyama, Shuichi Maeda, Qi Gao, Kenichi Koizumi, Yuki Sakamoto, Yuuki Ono, and Shinichiro Nakamura
ACS Omega 2022, 7, 10, 8581–8590
AI/ML Published: (Jan/2022)

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.

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