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Christian Feldmann

Showing results (1-10 of 16) with videos related to

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Iscience|September 15, 2022
Calculation of exact Shapley values for support vector machines with Tanimoto kernel enables model interpretationChristian Feldmann, Jürgen Bajorath
Scientific Reports|April 13, 2021
Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinationsChristian Feldmann, Jürgen Bajorath
Molecular Informatics|August 24, 2022
Advances in Computational PolypharmacologyChristian Feldmann, Jürgen Bajorath
International Journal of Molecular Sciences|May 31, 2020
X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying ShapesChristian Feldmann, Jürgen Bajorath
Molecules (Basel, Switzerland)|February 16, 2020
Biological Activity Profiles of Multitarget Ligands from X-ray StructuresChristian Feldmann, Jürgen Bajorath
Biomolecules|April 23, 2022
Differentiating Inhibitors of Closely Related Protein Kinases with Single- or Multi-Target Activity via Explainable Machine Learning and Feature AnalysisChristian Feldmann, Jürgen Bajorath
Biomolecules|December 2, 2020
Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine LearningChristian Feldmann, Dimitar Yonchev, Jürgen Bajorath
Scientific Reports|November 4, 2021
Explainable machine learning predictions of dual-target compounds reveal characteristic structural featuresChristian Feldmann, Maren Philipps, Jürgen Bajorath
Future Science OA|May 28, 2021
Structured data sets of compounds with multi-target and corresponding single-target activity from biological assaysChristian Feldmann, Dimitar Yonchev, Jürgen Bajorath
Molecular Informatics|September 4, 2020
Prediction of Promiscuity Cliffs Using Machine LearningThomas Blaschke, Christian Feldmann, Jürgen Bajorath
Pageof 2

Showing results (1-10 of 16) with videos related to

Sort By:
Pageof 2
Iscience|September 15, 2022
Calculation of exact Shapley values for support vector machines with Tanimoto kernel enables model interpretationChristian Feldmann, Jürgen Bajorath
Scientific Reports|April 13, 2021
Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinationsChristian Feldmann, Jürgen Bajorath
Molecular Informatics|August 24, 2022
Advances in Computational PolypharmacologyChristian Feldmann, Jürgen Bajorath
International Journal of Molecular Sciences|May 31, 2020
X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying ShapesChristian Feldmann, Jürgen Bajorath
Molecules (Basel, Switzerland)|February 16, 2020
Biological Activity Profiles of Multitarget Ligands from X-ray StructuresChristian Feldmann, Jürgen Bajorath
Biomolecules|April 23, 2022
Differentiating Inhibitors of Closely Related Protein Kinases with Single- or Multi-Target Activity via Explainable Machine Learning and Feature AnalysisChristian Feldmann, Jürgen Bajorath
Biomolecules|December 2, 2020
Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine LearningChristian Feldmann, Dimitar Yonchev, Jürgen Bajorath
Scientific Reports|November 4, 2021
Explainable machine learning predictions of dual-target compounds reveal characteristic structural featuresChristian Feldmann, Maren Philipps, Jürgen Bajorath
Future Science OA|May 28, 2021
Structured data sets of compounds with multi-target and corresponding single-target activity from biological assaysChristian Feldmann, Dimitar Yonchev, Jürgen Bajorath
Molecular Informatics|September 4, 2020
Prediction of Promiscuity Cliffs Using Machine LearningThomas Blaschke, Christian Feldmann, Jürgen Bajorath
Pageof 2