F. Rippmann

发表

Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay.

B. Merget, S. Turk, Sameh Eid, 2017, Journal of medicinal chemistry.

Computational approaches to identifying and characterizing protein binding sites for ligand design

R. Wade, F. Rippmann, G. Cruciani, 2009, Journal of molecular recognition : JMR.

Pharmacophore alignment search tool: Influence of canonical atom labeling on similarity searching

Friedrich Rippmann, Gisbert Schneider, Matthias Rupp, 2010, J. Comput. Chem..

Short linear motif candidates in the cell entry system used by SARS-CoV-2 and their potential therapeutic implications

Lucía B. Chemes, T. Gibson, F. Rippmann, 2020, Science Signaling.

KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome.

F. Rippmann, Andrea Volkamer, A. Kooistra, 2022, Journal of chemical information and modeling.

KiSSim: Predicting off-targets from structural similarities in the kinome

F. Rippmann, Andrea Volkamer, A. Kooistra, 2021 .

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

Catherine Jorand Lebrun, Andrea Unzue Lopez, Daniel Kuhn, 2020, J. Chem. Inf. Model..

DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment

Daniel Kuhn, Matthias Rarey, Friedrich Rippmann, 2012, Bioinform..

Combining Global and Local Measures for Structure-Based Druggability Predictions

Daniel Kuhn, Matthias Rarey, Friedrich Rippmann, 2012, J. Chem. Inf. Model..

KinMap: a web-based tool for interactive navigation through human kinome data

S. Turk, Sameh Eid, F. Rippmann, 2017, BMC Bioinformatics.

LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins.

M Hendlich, F Rippmann, G Barnickel, 1997, Journal of molecular graphics & modelling.

TRAPP: A Tool for Analysis of Transient Binding Pockets in Proteins

Friedrich Rippmann, Stefan Richter, Rebecca C. Wade, 2013, J. Chem. Inf. Model..

Identification and Visualization of Kinase-Specific Subpockets

Friedrich Rippmann, Sameh Eid, Simone Fulle, 2016, J. Chem. Inf. Model..

A Normal Mode-Based Geometric Simulation Approach for Exploring Biologically Relevant Conformational Transitions in Proteins

Holger Gohlke, Friedrich Rippmann, Aqeel Ahmed, 2011, J. Chem. Inf. Model..

Perturbation Approaches for Exploring Protein Binding Site Flexibility to Predict Transient Binding Pockets.

Friedrich Rippmann, Daria B Kokh, Rebecca C Wade, 2016, Journal of chemical theory and computation.

Pocketome of Human Kinases: Prioritizing the ATP Binding Sites of (Yet) Untapped Protein Kinases for Drug Discovery

Friedrich Rippmann, Sameh Eid, Simone Fulle, 2015, J. Chem. Inf. Model..

Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies

F. Rippmann, Nels Thorsteinson, D. Nannemann, 2021, mAbs.

KinMap: a web-based tool for interactive navigation through human kinome data

Friedrich Rippmann, Sameh Eid, Simone Fulle, 2017, BMC Bioinformatics.

Predicting enzymatic function from global binding site descriptors

Daniel Kuhn, Matthias Rarey, Friedrich Rippmann, 2013, Proteins.

Interpretable Deep Learning in Drug Discovery pdf

Friedrich Rippmann, Sepp Hochreiter, Günter Klambauer, 2019, Explainable AI.

Protein pocket and ligand shape comparison and its application in virtual screening

Matthias Rarey, Friedrich Rippmann, Olivier Michielin, 2013, Journal of Computer-Aided Molecular Design.

Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting

Friedrich Rippmann, Thierry Hanser, Fabian P. Steinmetz, 2019, Journal of Cheminformatics.

11th German Conference on Chemoinformatics (GCC 2015)

Timothy Clark, Jonathan W. Essex, Peter Kolb, 2016, J. Cheminformatics.

SUMO – In Silico Sequence Assessment Using Multiple Optimization Parameters

Daniel Kuhn, F. Rippmann, A. Evers, 2022, bioRxiv.

Context-enriched molecule representations improve few-shot drug discovery

S. Hochreiter, Daniel Kuhn, G. Klambauer, 2023, ICLR.

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.

Lewis H. Mervin, Djork-Arné Clevert, T. Morawietz, 2023, Journal of chemical information and modeling.

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization

Friedrich Rippmann, Samo Turk, Simone Fulle, 2017, J. Chem. Inf. Model..

Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+

Daniel Kuhn, Friedrich Rippmann, Christina Schindler, 2017, Journal of Computer-Aided Molecular Design.

A generalized framework for embedding-based few-shot learning methods in drug discovery

S. Hochreiter, G. Klambauer, F. Rippmann, 2022 .