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Searching the chemical space for effective magnesium dissolution modulators: a deep learning approach using sparse features
Citation Link: https://doi.org/10.15480/882.8763
Publikationstyp
Journal Article
Publikationsdatum
2023-12-01
Sprache
English
Author
Enthalten in
Volume
7
Issue
1
Article Number
74
Citation
npj Materials Degradation 7 (1): 74 (2023-12-01)
Publisher DOI
Scopus ID
Publisher
Nature Publishing Group
Small organic molecules can alter the degradation rates of the magnesium alloy ZE41. However, identifying suitable candidate compounds from the vast chemical space requires sophisticated tools. The information contained in only a few molecular descriptors derived from recursive feature elimination was previously shown to hold the potential for determining such candidates using deep neural networks. We evaluate the capability of these networks to generalise by blind testing them on 15 randomly selected, completely unseen compounds. We find that their generalisation ability is still somewhat limited, most likely due to the relatively small amount of available training data. However, we demonstrate that our approach is scalable; meaning deficiencies caused by data limitations can presumably be overcome as the data availability increases. Finally, we illustrate the influence and importance of well-chosen descriptors towards the predictive power of deep neural networks.
Schlagworte
MLE@TUHH
DDC Class
620: Engineering
Publication version
publishedVersion
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s41529-023-00391-0.pdf
Type
main article
Size
1.48 MB
Format
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