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Open AccessArticle

VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder

1
Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK
2
Department of Chemistry, The Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester M1 7DN, UK
3
Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs Lyngby, Denmark
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Present address: University College London Hospital NHS Foundation Trust, 250 Euston Road, London NW1 2PB, UK.
Academic Editor: Derek J. McPhee
Molecules 2020, 25(15), 3446; https://doi.org/10.3390/molecules25153446
Received: 26 June 2020 / Revised: 21 July 2020 / Accepted: 28 July 2020 / Published: 29 July 2020
(This article belongs to the Section Chemical Biology)
Molecular similarity is an elusive but core “unsupervised” cheminformatics concept, yet different “fingerprint” encodings of molecular structures return very different similarity values, even when using the same similarity metric. Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none are “better” than the others, nor than encoding-free metrics such as maximum common substructure (MCSS). We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE). This learns the joint distribution p(z|x) where z is a latent vector and x are the (same) input/output data. It takes the form of a “bowtie”-shaped artificial neural network. In the middle is a “bottleneck layer” or latent vector in which inputs are transformed into, and represented as, a vector of numbers (encoding), with a reverse process (decoding) seeking to return the SMILES string that was the input. We train a VAE on over six million druglike molecules and natural products (including over one million in the final holdout set). The VAE vector distances provide a rapid and novel metric for molecular similarity that is both easily and rapidly calculated. We describe the method and its application to a typical similarity problem in cheminformatics. View Full-Text
Keywords: cheminformatics; molecular similarity; deep learning; variational autoencoder; SMILES cheminformatics; molecular similarity; deep learning; variational autoencoder; SMILES
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MDPI and ACS Style

Samanta, S.; O’Hagan, S.; Swainston, N.; Roberts, T.J.; Kell, D.B. VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder. Molecules 2020, 25, 3446. https://doi.org/10.3390/molecules25153446

AMA Style

Samanta S, O’Hagan S, Swainston N, Roberts TJ, Kell DB. VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder. Molecules. 2020; 25(15):3446. https://doi.org/10.3390/molecules25153446

Chicago/Turabian Style

Samanta, Soumitra; O’Hagan, Steve; Swainston, Neil; Roberts, Timothy J.; Kell, Douglas B. 2020. "VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder" Molecules 25, no. 15: 3446. https://doi.org/10.3390/molecules25153446

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