ArticleJournal of cheminformatics2021
Translating the InChI: adapting neural machine translation to predict IUPAC names from a chemical identifier.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- DrugDL: dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.Bioinformatics (Oxford, England) · 2026Article
- Multifaceted Integrated Analysis of CDK1 and TOP2A Signaling Pathways for Multi-Target Therapeutic Intervention in Epithelial Ovarian Cancer.International journal of molecular sciences · 2026Article
- Article
- Integrated Analysis, Machine Learning, Molecular Docking and Dynamics of CDK1 Inhibitors in Epithelial Ovarian Cancer: A Multifaceted Approach Towards Targeted Therapy.International journal of molecular sciences · 2025Article
- STOUT V2.0: SMILES to IUPAC name conversion using transformer models.Journal of cheminformatics · 2024Article
- DrugSynthMC: An Atom-Based Generation of Drug-like Molecules with Monte Carlo Search.Journal of chemical information and modeling · 2024Article
- Application of Transformers in Cheminformatics.Journal of chemical information and modeling · 2024Review
- Molecular Identification from AFM Images Using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks.ACS applied materials & interfaces · 2023Article
- PubChem 2023 update.Nucleic acids research · 2023Article
- STOUT: SMILES to IUPAC names using neural machine translation.Journal of cheminformatics · 2021Article
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Authors and funding
4 authors.
Funding
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.
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