ArticlePloS one2021
A community-powered search of machine learning strategy space to find NMR property prediction models.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Machine learning spectroscopy to advance computation and analysis.Chemical science · 2025Review
- Spiers Memorial Lecture: NMR crystallography.Faraday discussions · 2025Article
- Prediction of transition state structures of gas-phase chemical reactions via machine learning.Nature communications · 2023Article
- Real-time prediction ofChemical science · 2021Article
- Predicting scalar coupling constants by graph angle-attention neural network.Scientific reports · 2021Article
- Ab Initio Machine Learning in Chemical Compound Space.Chemical reviews · 2021Review
- Predicting chemical shifts with graph neural networks.Chemical science · 2021Article
Corrections and comments
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Authors and funding
27 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published 'in-house' efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.
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Registered trials
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