Chemical Variational Autoencoder (chemical_VAE) is a free, open-source software for machine learning of molecular properties. chemical_VAE utilizes molecular SMILES that are encoded into a code vector representation and can be decoded from the code representation back to molecular SMILES. The autoencoder may also be jointly trained with property prediction to help shape the latent space. The new latent space can then be optimized upon to find the molecules with the most optimized properties of interest.
R. Gómez-Bombarelli, J. Wei, D. Duvenaud, J. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. Hirzel, R. Adams, and A. Aspuru-Guzik, "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules," ACS Central Science 4, 268-276 (2018). DOI: 10.1021/acscentsci.7b00572