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494 |
Sicho, M.; de Bruyn Kops, C.; Stork, C.; Svozil, D.; Kirchmair, J.
FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity
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2017494 |
Sicho, M.; de Bruyn Kops, C.; Stork, C.; Svozil, D.; Kirchmair, J. (2017) FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity . Journal of Chemical Information and Modeling, 57(8):1832-1846. |
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502 |
Stork, C.; Wagner, J.; Friedrich, N.-O.; de Bruyn Kops, C.; Sicho, M.; Kirchmair, J.
Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters
ChemMedChem: in press. |
2018502 |
Stork, C.; Wagner, J.; Friedrich, N.-O.; de Bruyn Kops, C.; Sicho, M.; Kirchmair, J. (2018) Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters . ChemMedChem:in press. |
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519 |
Escribano, A.; Steenbock, T.; Stork, C.; Herrmann, C.; Heck, J.
Why Are Dithienylethene-Linked Biscobaltocenes so Hard to Photoswitch?
ChemPhysChem, 18: 596-609. |
2017519 |
Escribano, A.; Steenbock, T.; Stork, C.; Herrmann, C.; Heck, J. (2017) Why Are Dithienylethene-Linked Biscobaltocenes so Hard to Photoswitch? . ChemPhysChem, 18:596-609. |
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520 |
Stork, C.; Kirchmair, J.
PAIN(S) relievers for medicinal chemists: how computational methods can assist in hit evaluation
Future Medicinal Chemistry, 10(13) |
2018520 |
Stork, C.; Kirchmair, J. (2018) PAIN(S) relievers for medicinal chemists: how computational methods can assist in hit evaluation . Future Medicinal Chemistry, 10(13) |
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526 |
Stork, C.; Chen, Y.; Sicho, M.; Kirchmair, J.
Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters
Journal of Chemical Information and Modeling |
2019526 |
Stork, C.; Chen, Y.; Sicho, M.; Kirchmair, J. (2019) Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters . Journal of Chemical Information and Modeling |
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529 |
Chen, Y.; Stork, C.; Hirte, S.; Kirchmair, J.
NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules
Biomolecules, 9(43) |
2019529 |
Chen, Y.; Stork, C.; Hirte, S.; Kirchmair, J. (2019) NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules . Biomolecules, 9(43) |
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539 |
Wilm, A.; Stork, C.; Bauer, C.; Schepky, A.; Kühnl, J.; Kirchmair, J.
Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability
International Journal of Molecular Sciences, 20(19) |
2019539 |
Wilm, A.; Stork, C.; Bauer, C.; Schepky, A.; Kühnl, J.; Kirchmair, J. (2019) Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability . International Journal of Molecular Sciences, 20(19) |
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554 |
Wilm, A.; Norinder, U.; Agea, M.I.; de Bruyn Kops, C.; Stork, C.; Kühnl, J.; Kirchmair, J.
Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules
Chemical Research in Toxicology, 34(2): 330-344. |
2021554 |
Wilm, A.; Norinder, U.; Agea, M.I.; de Bruyn Kops, C.; Stork, C.; Kühnl, J.; Kirchmair, J. (2021) Skin Doctor CP: Conformal Prediction of the Skin Sensitization Potential of Small Organic Molecules . Chemical Research in Toxicology, 34(2):330-344. |
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555 |
Wilm, A.; Garcia de Lomana, M.; Stork, C.; Mathai, N.; Hirte, S.; Norinder, U.; Kühnl, J.; Kirchmair, J.
Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors
Pharmaceuticals, 14(8) |
2021555 |
Wilm, A.; Garcia de Lomana, M.; Stork, C.; Mathai, N.; Hirte, S.; Norinder, U.; Kühnl, J.; Kirchmair, J. (2021) Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors . Pharmaceuticals, 14(8) |