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Splitting chemical structure data sets for federated privacy-preserving machine learning.

J. Simm, L. Humbeck, A. Zalewski, N. Sturm, W. Heyndrickx, Y. Moreau, B. Beck, and A. Schuffenhauer. J. Cheminformatics, 13 (1): 96 (2021)

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Reinhold Noé

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Splitting chemical structure data sets for federated privacy-preserving machine learning.J. Simm, L. Humbeck, A. Zalewski, N. Sturm, W. Heyndrickx, Y. Moreau, B. Beck, and A. Schuffenhauer. J. Cheminformatics, 13 (1): 96 (2021)Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability.O. Laufkötter, N. Sturm, J. Bajorath, H. Chen, and O. Engkvist. J. Cheminformatics, 11 (1): 54:1-54:14 (2019)Industry-scale application and evaluation of deep learning for drug target prediction.N. Sturm, A. Mayr, T. Van, V. Chupakhin, H. Ceulemans, J. Wegner, J. Dzib, N. Jeliazkova, Y. Vandriessche, S. Böhm and 9 other author(s). J. Cheminformatics, 12 (1): 26 (2020)Industry-Scale Orchestrated Federated Learning for Drug Discovery.M. Oldenhof, G. Ács, B. Pejó, A. Schuffenhauer, N. Holway, N. Sturm, A. Dieckmann, O. Fortmeier, E. Boniface, C. Mayer and 37 other author(s). AAAI, page 15576-15584. AAAI Press, (2023)MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.W. Heyndrickx, L. Mervin, T. Morawietz, N. Sturm, L. Friedrich, A. Zalewski, A. Pentina, L. Humbeck, M. Oldenhof, R. Niwayama and 39 other author(s). J. Chem. Inf. Model., 64 (7): 2331-2344 (2024)Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions.M. Trapotsi, L. Mervin, A. Afzal, N. Sturm, O. Engkvist, I. Barrett, and A. Bender. J. Chem. Inf. Model., 61 (3): 1444-1456 (2021)Industry-Scale Orchestrated Federated Learning for Drug Discovery.M. Oldenhof, G. Ács, B. Pejó, A. Schuffenhauer, N. Holway, N. Sturm, A. Dieckmann, O. Fortmeier, E. Boniface, C. Mayer and 35 other author(s). CoRR, (2022)Exploration and Comparison of the Geometrical and Physicochemical Properties of an αC Allosteric Pocket in the Structural Kinome.N. Sturm, A. Tinivella, and G. Rastelli. J. Chem. Inf. Model., 58 (5): 1094-1103 (2018)Structural Insights into the Molecular Basis of the Ligand Promiscuity.N. Sturm, J. Desaphy, R. Quinn, D. Rognan, and E. Kellenberger. J. Chem. Inf. Model., 52 (9): 2410-2421 (2012)Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.N. Sturm, J. Sun, Y. Vandriessche, A. Mayr, G. Klambauer, L. Carlsson, O. Engkvist, and H. Chen. J. Chem. Inf. Model., 59 (3): 962-972 (2019)

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Splitting chemical structure data sets for federated privacy-preserving machine learning.

Please choose a person to relate this publication to

Reinhold Noé

Roswitha Noé

Rolf Noé

Kirsten Noé

Haile Noé

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Sturm, Noé"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Splitting chemical structure data sets for federated privacy-preserving machine learning.

Please choose a person to relate this publication to

Reinhold Noé

Roswitha Noé

Rolf Noé

Kirsten Noé

Haile Noé

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
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APA
Chicago
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Splitting chemical structure data sets for federated privacy-preserving machine learning.