Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries. However, these VAEs are hindered by the non-unique nature of SMILES strings and the computational cost of graph convolutions. To efficiently pass messages along all paths through the molecular graph, we encode multiple SMILES strings of a single molecule using a set of stacked recurrent neural networks, harmonizing hidden representations of each atom between SMILES representations, and use attentional pooling to build a final fixed-length latent representation. By then decoding to a disjoint set of SMILES strings of the molecule, our All SMILES VAE learns an almost bijective mapping between molecules and latent representations near the high-probability-mass subspace of the prior. Our SMILES-derived but molecule-based latent representations significantly surpass the state-of-the-art in a variety of fully- and semi-supervised property regression and molecular property optimization tasks.
We pool messages amongst multiple SMILES strings of the same molecule to pass information along all paths through the molecular graph, producing latent representations that significantly surpass the state-of-the-art in a variety of tasks. Method uses multiple inputs of SMILES strings, character-wise feature fusion across those strings, and network training through multiple output targets of SMILES strings, creating a robust fixed-length latent representation independent of SMILES variation.   The authors describe a novel variational autoencoder like method for molecules which encode molecules as strings to reduce the operations needed to share information across atoms in the molecule.