Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier.

ABSTRACT: We present a sequence-to-sequence machine learning model for predicting the IUPAC name of a chemical from its standard International Chemical Identifier (InChI). The model uses two stacks of transformers in an encoder-decoder architecture, a setup similar to the neural networks used in state-of-the-art machine translation. Unlike neural machine translation, which usually tokenizes input and output into words or sub-words, our model processes the InChI and predicts the IUPAC name character by character. The model was trained on a dataset of 10 million InChI/IUPAC name pairs freely downloaded from the National Library of Medicine's online PubChem service. Training took seven days on a Tesla K80 GPU, and the model achieved a test set accuracy of 91%. The model performed particularly well on o

SUBMITTER: Handsel J 

PROVIDER: S-EPMC8496104 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature