Christian Feldmann

发表

Mapping the S1 and S1’ subsites of cysteine proteases with new dipeptidyl nitrile inhibitors as trypanocidal agents

J. Bajorath, Erik Gilberg, M. Gütschow, 2020, PLoS neglected tropical diseases.

Mapping the S1 and S1’ subsites of cysteine proteases with new dipeptidyl nitrile inhibitors as trypanocidal agents

J. Bajorath, Erik Gilberg, M. Gütschow, 2019, bioRxiv.

Calculation of exact Shapley values for support vector machines with Tanimoto kernel enables model interpretation

J. Bajorath, Christian Feldmann, 2022, iScience.

EdgeSHAPer: Bond-centric Shapley value-based explanation method for graph neural networks

J. Bajorath, Raquel Rodríguez-Pérez, Christian Feldmann, 2022, iScience.

N-Sulfonyl dipeptide nitriles as inhibitors of human cathepsin S: In silico design, synthesis and biochemical characterization.

J. Bajorath, Erik Gilberg, M. Gütschow, 2020, Bioorganic & medicinal chemistry letters.

Minimal data requirements for accurate compound activity prediction using machine learning methods of different complexity

J. Bajorath, Christian Feldmann, Friederike Maite Siemers, 2022, Cell Reports Physical Science.

Can Cysteine Protease Cross-class Inhibitors Achieve Selectivity?

J. Bajorath, Erik Gilberg, M. Gütschow, 2019, Journal of medicinal chemistry.

Compounds with multitarget activity: structure-based analysis and machine learning

Jürgen Bajorath, Christian Feldmann, J. Bajorath, 2020 .

Explainable machine learning predictions of dual-target compounds reveal characteristic structural features

J. Bajorath, M. Philipps, Christian Feldmann, 2021, Scientific Reports.

Machine learning reveals that structural features distinguishing promiscuous and non-promiscuous compounds depend on target combinations

J. Bajorath, Christian Feldmann, 2021, Scientific Reports.

Prediction of Promiscuity Cliffs Using Machine Learning

Christian Feldmann, Thomas Blaschke, Juergen Bajorath, 2020, Molecular informatics.

Structured data sets of compounds with multi-target and corresponding single-target activity from biological assays

Jürgen Bajorath, Dimitar Yonchev, Christian Feldmann, 2021, Future science OA.

X-ray Structure-Based Chemoinformatic Analysis Identifies Promiscuous Ligands Binding to Proteins from Different Classes with Varying Shapes

Jürgen Bajorath, Christian Feldmann, J. Bajorath, 2020, International journal of molecular sciences.

Biological Activity Profiles of Multitarget Ligands from X-ray Structures

Jürgen Bajorath, Christian Feldmann, J. Bajorath, 2020, Molecules.

Identifying Promiscuous Compounds with Activity against Different Target Classes

Jürgen Bajorath, Dimitar Yonchev, Filip Miljković, 2019, Molecules.

Differentiating Inhibitors of Closely Related Protein Kinases with Single- or Multi-Target Activity via Explainable Machine Learning and Feature Analysis

J. Bajorath, Christian Feldmann, 2022, Biomolecules.

Analysis of Biological Screening Compounds with Single- or Multi-Target Activity via Diagnostic Machine Learning

Jürgen Bajorath, Christian Feldmann, Dimitar Yonchev, 2020, Biomolecules.

Advances in Computational Polypharmacology

J. Bajorath, Christian Feldmann, 2022, Molecular informatics.

Systematic Data Analysis and Diagnostic Machine Learning Reveal Differences between Compounds with Single- and Multitarget Activity.

Jürgen Bajorath, Christian Feldmann, Dimitar Yonchev, 2020, Molecular pharmaceutics.

Optimizing over trained GNNs via symmetry breaking

R. Misener, D. Walz, M. Mathea, 2023, NeurIPS.