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 organics, with the exception of macrocycles, and was comparable to commercial IUPAC name generation software. The predictions were less accurate for inorganic and organometallic compounds. This can be explained by inherent limitations of standard InChI for representing inorganics, as well as low coverage in the training data.

Molecular Identification from AFM images using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks

“…We have already pointed out that, leaving out the additional problem of extracting the chemical information from the images, a RNN only achieves a BLEU 4-gram score of 0.86 when translating from the SMILES to the IUPAC name (58). Nomenclature translation has been addressed with architectures based on the novel transformer networks (62), obtaining a practically perfect accuracy (63,64). Also, automatic recognition of molecular graphical depictions is able to correctly translate them to their SMILES representation with a 88% or 96% accuracy by using either a standard encoder-decoder (46) or a transformer (45) network.…”

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

“…From the perspective of life science, the properties of molecules and the effects of drugs are mostly determined by their 3D structures [14, 15]. In most current MRL methods, one starts with representing molecules as 1D sequential strings, such as SMILES [16,17,18] and InChI [19,20,21], or 2D graphs [22,11,23,12]. This may limit their ability to incorporate 3D information for downstream tasks.…”

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

“…From the perspective of life science, the properties of molecules and the effects of drugs are mostly determined by their 3D structures [14, 15]. In most current MRL methods, one starts with representing molecules as 1D sequential strings, such as SMILES [16,17,18] and InChI [19,20,21], or 2D graphs [22,11,23,12,24]. This may limit their ability to incorporate 3D information for downstream tasks.…”