Published December 19, 2019
| Version v 1.0
Dataset
Open
Druggability Assessment in TRAPP using Machine Learning Approaches
Authors/Creators
- 1. Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany; Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, 69120 Heidelberg, Germany
- 2. Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany; Faculty of Biosciences, Heidelberg University, 69120, Heidelberg, Germany
- 3. Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany
- 4. Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany Zentrum für Molekulare Biologie (ZMBH), DKFZ-ZMBH Alliance, Heidelberg University, 69120 Heidelberg, Germany Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, 69120 Heidelberg, Germany Faculty of Biosciences, Heidelberg University, 69120, Heidelberg, Germany
Description
This archive contains data sets and software codes used for building ML models reported in the paper "Druggability Assessment in TRAPP using Machine Learning Approaches"; J. Chem. Inf. Model. 2020, 60, 3, 1685–1699; https://doi.org/10.1021/acs.jcim.9b01185
Files
Files
(8.4 GB)
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md5:1886dcc73a86f54056be5defeba2804a
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3.1 GB | Download |
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md5:afc9550d3282c9f656ab014d3d1b300b
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15.8 MB | Download |
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md5:46ddcf134ece5e2116e41340589d9c20
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37.3 MB | Download |
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md5:0a811e8d89f15c9a8bcf0d68ae3aebe2
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1.5 MB | Download |
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md5:a10abefbcf8520692a861c69c8ea7f70
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117.0 MB | Download |
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md5:e2476b055fb5c7d0cc819dcf64ff8763
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238.4 MB | Download |
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md5:ad618e41cf561740e36d78763340e60a
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3.3 GB | Download |
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md5:ec87699ea6b77256630fb61b860297a4
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1.6 GB | Download |
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md5:19e9af7fc8d007d3c9d68cf94549cdc3
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4.9 kB | Download |
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md5:365fcc853d590c590adcbf3fd00c825d
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146.2 kB | Download |
Additional details
Related works
- Is supplement to
- Other: 10.1101/2019.12.19.882340 (DOI)
- Journal article: 10.1021/acs.jcim.9b01185 (DOI)