All SMILES Variational Autoencoder

By Zaccary Alperstein, Artem Cherkasov, Jason Tyler Rolfe. 2019

43 citations recorded by the source corpus.

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, pooling 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.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Generative Models, Computational Chemistry & Drug Discovery

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Citations in the corpus

Score-based Generative Modeling in Latent Space (2021, neutral): “In principle LSGM could be used to model, for example, language [96, 97], music [98, 10], or molecules [99, 100].”
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection (2019, background): “…space Z too far away from the training data, which might yield latent points z that decode to semantically meaningless and/or syntactically invalid inputs x (Kusner et al., 2017; Grifﬁths & Hern´andez-Lobato, 2017; Brookes et al., 2019; Mahmood & Hern´andez-Lobato, 2019; Alperstein et al., 2019).”
BERT Learns (and Teaches) Chemistry (2020, methodology): “One work proposes a very elaborate and hand-designed architecture that performs well on regression tasks using SMILES string [1].”