KinMap: a web-based tool for interactive navigation through human kinome data von Eid, Sameh, Turk, Samo, Volkamer, Andrea, Rippmann, Friedrich, Fulle, Simone

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DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment von VOLKAMER, Andrea, KUHN, Daniel, RIPPMANN, Friedrich, RAREY, Matthias

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Combining Global and Local Measures for Structure-Based Druggability Predictions von Volkamer, Andrea, Kuhn, Daniel, Grombacher, Thomas, Rippmann, Friedrich, Rarey, Matthias

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Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay von Merget, Benjamin, Turk, Samo, Eid, Sameh, Rippmann, Friedrich, Fulle, Simone

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Computational approaches to identifying and characterizing protein binding sites for ligand design von Henrich, Stefan, Salo-Ahen, Outi M. H., Huang, Bingding, Rippmann, Friedrich F., Cruciani, Gabriele, Wade, Rebecca C.

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Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies von Grinshpun, Boris, Thorsteinson, Nels, Pereira, Joao Ns, Rippmann, Friedrich, Nannemann, David, Sood, Vanita D, Fomekong Nanfack, Yves

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Identification and Visualization of Kinase-Specific Subpockets von Volkamer, Andrea, Eid, Sameh, Turk, Samo, Rippmann, Friedrich, Fulle, Simone

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Pocketome of Human Kinases: Prioritizing the ATP Binding Sites of (Yet) Untapped Protein Kinases for Drug Discovery von Volkamer, Andrea, Eid, Sameh, Turk, Samo, Jaeger, Sabrina, Rippmann, Friedrich, Fulle, Simone

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Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting von Hanser, Thierry, Steinmetz, Fabian P., Plante, Jeffrey, Rippmann, Friedrich, Krier, Mireille

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LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins von Hendlich, Manfred, Rippmann, Friedrich, Barnickel, Gerhard

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Interpretable Deep Learning in Drug Discovery von Preuer, Kristina, Klambauer, Günter, Rippmann, Friedrich, Hochreiter, Sepp, Unterthiner, Thomas

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Protein pocket and ligand shape comparison and its application in virtual screening von Wirth, Matthias, Volkamer, Andrea, Zoete, Vincent, Rippmann, Friedrich, Michielin, Olivier, Rarey, Matthias, Sauer, Wolfgang H. B.

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Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP von Schindler, Christina, Rippmann, Friedrich, Kuhn, Daniel

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KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome von Sydow, Dominique, Aßmann, Eva, Kooistra, Albert J., Rippmann, Friedrich, Volkamer, Andrea

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Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects von Schindler, Christina E. M, Baumann, Hannah, Blum, Andreas, Böse, Dietrich, Buchstaller, Hans-Peter, Burgdorf, Lars, Cappel, Daniel, Chekler, Eugene, Czodrowski, Paul, Dorsch, Dieter, Eguida, Merveille K. I, Follows, Bruce, Fuchß, Thomas, Grädler, Ulrich, Gunera, Jakub, Johnson, Theresa, Jorand Lebrun, Catherine, Karra, Srinivasa, Klein, Markus, Knehans, Tim, Koetzner, Lisa, Krier, Mireille, Leiendecker, Matthias, Leuthner, Birgitta, Li, Liwei, Mochalkin, Igor, Musil, Djordje, Neagu, Constantin, Rippmann, Friedrich, Schiemann, Kai, Schulz, Robert, Steinbrecher, Thomas, Tanzer, Eva-Maria, Unzue Lopez, Andrea, Viacava Follis, Ariele, Wegener, Ansgar, Kuhn, Daniel

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Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization von Turk, Samo, Merget, Benjamin, Rippmann, Friedrich, Fulle, Simone

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A Normal Mode-Based Geometric Simulation Approach for Exploring Biologically Relevant Conformational Transitions in Proteins von Ahmed, Aqeel, Rippmann, Friedrich, Barnickel, Gerhard, Gohlke, Holger

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Short linear motif candidates in the cell entry system used by SARS-CoV-2 and their potential therapeutic implications von Mészáros, Bálint, Sámano-Sánchez, Hugo, Alvarado-Valverde, Jesús, Čalyševa, Jelena, Martínez-Pérez, Elizabeth, Alves, Renato, Shields, Denis C, Kumar, Manjeet, Rippmann, Friedrich, Chemes, Lucía B, Gibson, Toby J

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Mechanism of Praziquantel Action at a Transient Receptor Potential Channel von Park, Sang-Kyu, Friedrich, Lukas, Yahya, Nawal, Rohr, Claudia, Maillard, David, Rippmann, Friedrich, Spangenberg, Thomas, Marchant, Jonathan S.

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MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information von Heyndrickx, Wouter, Mervin, Lewis, Morawietz, Tobias, Sturm, Noé, Friedrich, Lukas, Zalewski, Adam, Pentina, Anastasia, Humbeck, Lina, Oldenhof, Martijn, Niwayama, Ritsuya, Schmidtke, Peter, Fechner, Nikolas, Simm, Jaak, Arany, Adam, Drizard, Nicolas, Jabal, Rama, Afanasyeva, Arina, Loeb, Regis, Verma, Shlok, Harnqvist, Simon, Holmes, Matthew, Pejo, Balazs, Telenczuk, Maria, Holway, Nicholas, Dieckmann, Arne, Rieke, Nicola, Zumsande, Friederike, Clevert, Djork-Arné, Krug, Michael, Luscombe, Christopher, Green, Darren, Ertl, Peter, Antal, Peter, Marcus, David, Do Huu, Nicolas, Fuji, Hideyoshi, Pickett, Stephen, Acs, Gergely, Boniface, Eric, Beck, Bernd, Sun, Yax, Gohier, Arnaud, Rippmann, Friedrich, Engkvist, Ola, Göller, Andreas H., Moreau, Yves, Galtier, Mathieu N., Schuffenhauer, Ansgar, Ceulemans, Hugo

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