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Druggability Assessment in TRAPP using Machine Learning Approaches
View ORCID ProfileJui-Hung Yuan, View ORCID ProfileSungho Bosco Han, View ORCID ProfileStefan Richter, View ORCID ProfileRebecca C. Wade, View ORCID ProfileDaria B. Kokh
doi: https://doi.org/10.1101/2019.12.19.882340
Jui-Hung Yuan
†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
Sungho Bosco Han
†Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany
¶Faculty of Biosciences, Heidelberg University, 69120, Heidelberg, Germany
Stefan Richter
†Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany
Rebecca C. Wade
†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
Daria B. Kokh
†Molecular and Cellular Modeling Group, Heidelberg Institute of Theoretical Studies (HITS), 69118 Heidelberg, Germany
Posted December 20, 2019.
Druggability Assessment in TRAPP using Machine Learning Approaches
Jui-Hung Yuan, Sungho Bosco Han, Stefan Richter, Rebecca C. Wade, Daria B. Kokh
bioRxiv 2019.12.19.882340; doi: https://doi.org/10.1101/2019.12.19.882340
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