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VAE-Sim: a novel molecular similarity measure based on a variational autoencoder

Soumitra Samanta, Steve O’Hagan, Neil Swainston, Timothy J. Roberts, View ORCID ProfileDouglas B. Kell
doi: https://doi.org/10.1101/2020.06.26.172908
Soumitra Samanta
1Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK
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Steve O’Hagan
2Department of Chemistry, The University of Manchester, 131 Princess St, Manchester M1 7DN, UK
3The Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester M1 7DN, UK
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Neil Swainston
1Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK
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Timothy J. Roberts
1Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK
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Douglas B. Kell
1Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown St, Liverpool L69 7ZB, UK
4Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
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  • ORCID record for Douglas B. Kell
  • For correspondence: dbk@liv.ac.uk
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Abstract

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 is ‘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 6 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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 28, 2020.
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VAE-Sim: a novel molecular similarity measure based on a variational autoencoder
Soumitra Samanta, Steve O’Hagan, Neil Swainston, Timothy J. Roberts, Douglas B. Kell
bioRxiv 2020.06.26.172908; doi: https://doi.org/10.1101/2020.06.26.172908
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VAE-Sim: a novel molecular similarity measure based on a variational autoencoder
Soumitra Samanta, Steve O’Hagan, Neil Swainston, Timothy J. Roberts, Douglas B. Kell
bioRxiv 2020.06.26.172908; doi: https://doi.org/10.1101/2020.06.26.172908

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