An irregular sampler

Hoch, Jeffrey C.

doi:10.1002/cmr.a.21459

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…ML strongly depends on training data, and getting experimental spectra is expensive (specially to get them assigned or labeled) and may result in sparse and unbalanced data sets. An interesting ML exercise providing a valuable lesson on the importance of training data was recently reported by Hoch . They tried to apply a ML algorithm (the details of the actual ML algorithm have not been disclosed) to find optimal sampling schedules for non‐uniformly sampled (NUS) NMR spectra.…”

Section: Signal Processingmentioning

confidence: 99%

NMR signal processing, prediction, and structure verification with machine learning techniques

Cobas¹

2020

Magnetic Reson in Chemistry

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Machine learning (ML) methods have been present in the field of NMR since decades, but it has experienced a tremendous growth in the last few years, especially thanks to the emergence of deep learning (DL) techniques taking advantage of the increased amounts of data and available computer power. These algorithms are successfully employed for classification, regression, clustering, or dimensionality reduction tasks of large data sets and have been intensively applied in different areas of NMR including metabonomics, clinical diagnosis, or relaxometry. In this article, we concentrate on the various applications of ML/DL in the areas of NMR signal processing and analysis of small molecules, including automatic structure verification and prediction of NMR observables in solution. K E Y W O R D S

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Section: Signal Processingmentioning

confidence: 99%

NMR signal processing, prediction, and structure verification with machine learning techniques

Cobas¹

2020

Magnetic Reson in Chemistry

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“…[ 58–62 ] These applications have been successful enough that multiple versions of AlphaFold, developed by Google's DeepMind team, have performed best overall in the Critical Assessment of Techniques for Protein Structure Prediction (CASP) competition for two consecutive years. [ 63 ] AlphaFold2 has been used to predict the structures of all proteins in the human proteome plus those from an additional 20 organisms, resulting in 350 K structures initially and which has grown to 200 M. [ 59,64 ] Recently, it has been shown that protein sequences alone are sufficient to predict three‐dimensional structure in many cases. [ 61,62 ] While there is understandably a considerable amount of excitement for these achievements and their future implications, many challenges remain, especially as applied to drug discovery.…”

Section: Molecular Generationmentioning

confidence: 99%

The rise of the machines in chemistry

Gill

2022

Magnetic Reson in Chemistry

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The use of artificial intelligence and, more specifically, deep learning methods in chemistry is becoming increasingly common. Applications in informatics fields, such as cheminformatics and proteomics, structural biology, and spectroscopy, including NMR, are on the rise. Recent developments in model architectures, such as graph convolutional neural networks and transformers, have been enabled by advancements in computational hardware and software. However, model architectures with more predictive power often require larger amounts of training data, which can be challenging to acquire, but this requirement can be mitigated through techniques like pretraining and fine‐tuning. In spite of these successes, challenges remain, such as normalization and scaling of data, availability of experimentally acquired data, and model explainability.

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