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Graph isomorphism-based algorithm for cross-checking chemical and crystallographic descriptions.

Andrius MerkysAntanas VaitkusAlgirdas GrybauskasAleksandras KonovalovasMiguel QuirósSaulius Gražulis
Published in: Journal of cheminformatics (2023)
Published reports of chemical compounds often contain multiple machine-readable descriptions which may supplement each other in order to yield coherent and complete chemical representations. This publication presents a method to cross-check such descriptions using a canonical representation and isomorphism of molecular graphs. If immediate agreement between compound descriptions is not found, the algorithm derives the minimal set of simplifications required for both descriptions to arrive to a matching form (if any). The proposed algorithm is used to cross-check chemical descriptions from the Crystallography Open Database to identify coherently described entries as well as those requiring further curation.
Keyphrases
  • deep learning
  • machine learning
  • neural network
  • working memory
  • minimally invasive
  • systematic review
  • convolutional neural network