Patent application
• Koch, O., Maskri, S., Rescher, U., Raabe, C.A., Pajonczyk, D. Development of potent antagonists and partial agonists through structural modelling of binding characteristics of potent formylated FPR1 agonists, EP 21214029.7 filed 13 December 2021
• Koch, O., Bering, L. Mycobacterium tuberculosis Thioredoxin Reductase Inhibitor As Antituberculosis Drug,
EP 17 179 568.5 filed 04th July 2017 & PCT/EP2018/066768 filed 22th June 2018
Selected presentations
• AI in Pharmaceutical Sciences: Let's talk about data, Annual Meeting of the German Pharmaceutical Society (DPhG), Freiburg, Germany, 10/2025, invited.
• AI in Pharmaceutical Sciences: Let's talk about data, 6th Braunschweig International Symposium on Pharmaceutical Engineering Research, Braunschweig, Germany, 09/2025, invited.
• AI and Data-Driven Drug Development, XXIX National Meeting on Medicinal Chemistry, Parma, Italy, 09/2025, invited.
• Rational drug design: From crystallographic fragment screening to AI-based methods, efs-NeW Research Day, Erlangen, Germany, 07/2025, invited.
• Fragment-based Drug Design by Catalogue, Novartis AG, Basel, Switzerland, 06/2025, invited.
• Fragment-based Drug Design by Catalogue, F. Hoffmann-La Roche Ltd, Basel, Switzerland, 06/2025, invited.
• Fragment libraries: How sociable are they, and can we do better?, BESSY@HZB User Meeting, Helmholtz Centre Berlin, Germany, 12/2024, invited.
• AI in Drug Discovery - What can be done that could not be done before, ScaDS.AI Summer School, Jena, Germany, 06/2024, invited.
• AI in Drug Design - Let's Talk about Data, Beilstein Bozen Symposium “AI in Chemistry and Biology: Evolution or Revolution?”, Rüdesheim, Germany, 06/2024, invited.
• Artificial intelligence - The revolution in drug development? 13. Pfizer Inflammation Campus, Berlin, Germany, 06/2024, invited.
• Off-target prediction based on binding site similarity, 23rd EuroQSAR, Heidelberg, Germany, 09/2022, invited.
• Neural Fingerprints: Generating Novel Domain-Specific Molecular Fingerprints Using Neural Networks, Discovery Chemistry US, 11/2021, invited.
• The use of data-driven decisions for rational molecular design, MedChemCASES: GDCh online-seminar of the subdivision Medicinal chemistry, 10/2020, invited.
Authored and edited books, book chapters
10. C. Dunker, S. Gross, L. Vinnenberg, A.-K. Prinz, O. Koch, A. Junker, A. Physiological roles and therapeutic potential of the purinergic P2X7 receptor. In Comprehensive Medicinal Chemistry, 4th edition, 2026, 760-799, Elsevier. https://doi.org/10.1016/B978-0-443-29808-0.00063-7
9. M. Grieswelle, J. Kaminski, O. Koch. Eine Einführung in die künstliche Intelligenz, Pharmakon, 2025, 4, 259 – 265.
8. O. Koch, D. Kuhn (eds). Künstliche Intelligenz in der Pharmazie, Pharmakon, 2025, 4.
7. Droschinsky, A., Humbeck, L., Koch, O., Kriege, N.M., Mutzel, P., Schäfer, T. Graph-Based Methods for Rational Drug Design. In: Bast, H., Korzen, C., Meyer, U., Penschuck, M. (eds) Algorithms for Big Data. Lecture Notes in Computer Science, 2022, Vol 13201. Springer, Cham. https://doi.org/10.1007/978-3-031-21534-6_5
6. Kriege, N., Humbeck, L., Koch, O.* Chemical similarity and sub structure search. In: Ranganathan, S., Gribskov, M., Nakai, K. and Schönbach, C. (eds.), Encyclopedia of Bioinformatics and Computational Biology, 2019, Vol. 2, 640–649. Oxford: Elsevier. https://doi.org/10.1016/B978-0-12-809633-8.20195-7
5. Brinkjost, T., Ehrt, C., Koch, O.* Binding Site Comparison – Software and Applications. In: Ranganathan, S., Nakai, K., Schönbach C. and Gribskov, M. (eds.), Encyclopedia of Bioinformatics and Computational Biology, 2019, Vol. 2, pp. 650–660. Oxford: Elsevier. https://doi.org/10.1016/B978-0-12-809633-8.20196-9
4. Selzer, P.M., Marhöfer, R.J., Koch, O. Angewandte Bioinformatik: Eine Einführung, 2. Auflage, Springer Spektrum, Berlin, Germany, 2018.
http://dx.doi.org/10.1007/978-3-662-54135-7
3. Selzer, P.M., Marhöfer, R.J., Koch, O. Applied Bioinformatics: An Introduction, 2. Edition, Springer Nature, Cham, Switzerland, 2018. https://link.springer.com/book/10.1007/978-3-319-68301-0
2. Koch, O.*, Jäger, T., Flohé, L., Selzer, P.M. Inhibition of trypanothione synthetase as therapeutic concept. In Jäger, T., Koch, O., Flohé, L. (Eds) "Trypanosomatid Diseases: Molecular Routes to Drug Discovery", Wiley-VCH, Weinheim, Germany, 2013; 429-443.
http://dx.doi.org/10.1002/9783527670383.ch23
1. Jäger, T., Koch, O., Flohé, L.(Eds) Trypanosomatid Diseases: Molecular Routes to Drug Discovery, Wiley-VCH, Weinheim, Germany, 2013.
http://dx.doi.org/10.1002/9783527670383
Submitted/Preprint Manuscripts
• M. Gozzi, J. Massa, O. Koch*. Mutation-Induced Pocket Deactivation: How Ser353/Pro245 Alters KCa2.2 vs KCa3.1 Ligand Selectivity. BioRxiv. 2026. https://doi.org/10.64898/2026.05.03.722491
• Grieswelle, M., Homberg, S., Janssen, P., Kaminski, J., Massa, J., Georgiev, S., Koch, O. A new benchmark for deep learning based affinity prediction: Solving the inter-protein scoring noise problem. ChemRxiv. 2025. https://doi.org/10.26434/chemrxiv-2025-sf3cs
• A.K. Prinz, P. Janssen, F. Rosini, C. A. Montanari, O. Koch*. Promiscuous scaffolds: Friend or foe in fragment-based drug design? ChemRxiv. 2025. https://doi.org/10.26434/chemrxiv-2025-bmv1h
• J. Kaminski, O. Koch*. χ-SOM: Emergent self-organising Maps for large chemical space visualisation. ChemRxiv, 2026. https://doi.org/10.26434/chemrxiv.15000904/v1
• J. Massa, J. Hense, T. Gangnus, M. Gozzi, E. E. Bulk, B. B. Burckhardt, M. Düfer, A. Schwab, O. Koch*. Discovery of the first small-molecule extracellular inhibitor of KCa3.1, BioRxiv, 2026. https://doi.org/10.64898/2026.03.08.710400
• Prinz, A.-K., Janssen, P., Rosini, F., Montanari, C., Koch, O. Promiscuous scaffolds: Friend or foe in fragment-based drug design? ChemRxiv. 2025. https://doi.org/10.26434/chemrxiv-2025-bmv1h
• Janssen, P., Becker, F., Benz, L., Füsser, F.T., Tolmachova, N., Matviiuk, T., Kondratov, I., Weiss, M., Kümmel, D., Koch, O. Accessing Ultralarge Chemical Spaces via a Sociable Fragment Library: Design and Crystallographic Screening. ChemRxiv. 2025. https://doi.org/10.26434/chemrxiv-2024-rpst3-v3
• Homberg, S.K.R., Modlich, M.L., Menke, J., Morris, G.M., Risse, B., Koch, O. Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches. ChemRxiv. 2024. https://doi.org/10.26434/chemrxiv-2023-1hxxc-v2
Peer-Reviewed Publications
63. F. Victoria-Munoz, N. Sánchez-Cruz, O. Koch*. SMARTDock: A toolkit for the automated development of target-specific scoring functions using bioactivity data. JCIM, 2026, 66, 812–819. https://doi.org/10.1021/acs.jcim.5c01490
62. F. Victoria-Munoz, A.D. Torres-García, C. Sierra, O. Koch*, N. Sánchez-Cruz*. Harnessing the Potential of Natural Products in Insecticide Discovery: A Cheminformatics Approach. J. Agric. Food Chem. 2025, 73, 32995–33004. https://doi.org/10.1021/acs.jafc.5c03553
61. F. Victoria-Munoz, J. Menke, N. Sánchez-Cruz, O. Koch*. Efficient Decoy Selection to Improve Virtual Screening Using Machine Learning Models. J Cheminf. 2025, 17, 165. https://doi.org/10.1186/s13321-025-01107-z
60. Thale, I., Massa, J., Naß, E., Vinnenberg, L., Todesca, L.M., Budde, T., Maisuls, I., Strassert, C.A., Koch, O., Schwab, A., Wünsch, B. Visualization of KCa3.1 Channels in Tumor Cells by Optimized Senicapoc-Bodipy Conjugates. ACS Pharmacol Transl Sci. 2025, 8, 3371-3388. https://doi.org/10.1021/acsptsci.5c00510
59. Erlitz, K.S., Prinz, A.K., Wagner, S., Massa, J., Dunker, C., Höhl, M., Griep, A., McManus, R.M., Schelhaas, S., Koch, O., Junker, A. Naphtho[1,2-b][1,4]diazepinedione-Based P2X4 Receptor Antagonists from Structure-Activity Relationship Studies toward PET Tracer Development. J Med Chem. 2025, Epub ahead of print. https://doi.org/10.1021/acs.jmedchem.4c02171
58. Erlitz, K.S., Siutkina, A.I., Prinz, A.-K., Koch, O., Kalinin, D.V., Junker, A. Piperazine-based P2X4 receptor antagonists. Arch. Pharm. 2025, 358, e2400860. https://doi.org/10.1002/ardp.202400860
57. Homberg, S.K.R., Modlich, M. L., Becker, M., Morris, G., Risse, B., Koch, O.* Interpreting Graph Neural Networks with Myerson Values for Cheminformatics Approaches. In Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions, W. Senn et al. (Eds.), 2025, 232-234, LNCS 16072: Springer Nature.
56. Längle, D., Wojtowicz-Piotrowski, S., Priegann, T., Keller, N., Wesseler, F., Reckzeh, E.S., Steffens, K., Grathwol, C., Lemke, J., Flasshoff, M., Näther, C., Jonson, A.C., Link, A., Koch, O., Di Guglielmo, G.M., Schade, D.* Expanding the Chemical Space of Transforming Growth Factor-β (TGFβ) Receptor Type II Degraders with 3,4-Disubstituted Indole Derivatives. ACS Pharmacol. Transl. Sci. 2024, 7, 1069-1085. https://doi.org/10.1021/acsptsci.3c00371
55. Gómez-García, A., Prinz, A.-K., Jiménez, D.A.A., Zamora, W.J., Barazorda-Ccahuana, H.L., Chávez-Fumagalli, M.Á., Valli, M., Andricopulo, A.D., da S Bolzani, V., Olmedo, D.A., Solís, P.N., Núñez, M.J., Rodríguez Pérez, J.R., Sánchez, H.A.V., Cortés Hernández, H.F., Mosquera Martinez, O.M., Koch, O.*, Medina-Franco, J.L.* Updating and profiling the natural product-likeness of Latin American compound libraries. Mol. Inform. 2024, 43, e202400052. https://doi.org/10.1002/minf.202400052
54. Füsser, F.T., Wollenhaupt, J., Weiss, M.S., Kümmel, D.*, Koch, O.* Novel starting points for fragment-based drug design against mycobacterial thioredoxin reductase identified using crystallographic fragment screening. Acta Crystallogr. D Struct. Biol. 2023, 79, 857-865. https://doi.org/10.1107/S2059798323005223
53. Zhang, Y., Menke, J., He, J., Nittinger, E., Tyrchan, C., Koch, O., Zhao, H. Similarity-based pairing improves efficiency of siamese neural networks for regression tasks and uncertainty quantification. J. Cheminform. 2023, 15, 75. https://doi.org/10.1186/s13321-023-00744-6
52. Menke, J., Homberg, S., Koch, O.*. Introduction to Artificial Intelligence and Deep Learning using Interactive Electronic Notebooks. Arch. Pharm, 2023, 2022, 355, 2200388. https://doi.org/10.1002/ardp.202200628
51. Isaak, A., Dobelmann, C., Füsser, F. T., Erlitz, K. S., Koch, O., Junker, A. Unveiling the Structure–Activity Relationships at the Orthosteric Binding Site of P2X Ion Channels: The Route to Selectivity. J. Med. Chem. 2022, 65, 11291-11308. https://doi.org/10.1021/acs.jmedchem.2c00812
50. Thale, I., Maskri, S., Grey, L., Todesca, L.M., Budde, T., Maisuls, I., Strassert, C.A., Koch, O., Schwab, A., Wünsch, B. Imaging of KCa3.1 channels in tumor cells with PET and small-molecule fluorescent probes. ChemMedChem. 2022, 18, e202200551. https://doi.org/10.1002/cmdc.202200551
49. Konken. C. P., Heßling, K., Thale, I., Schelhaas, S., Dabel, J., Maskri, S., Bulk, E., Budde, T., Koch, O., Schwab, A., Schäfers, M., Wünsch, B. Imaging of the calcium activated potassium channel 3.1 (KCa3.1) in vivo using a senicapoc-derived positron emission tomography tracer. Arch. Pharm. 2022, 355, e2200388. https://doi.org/10.1002/ardp.202200388
48. Wiedemann,B. , Kamps, D., Depta, L. , Weisner, J., Cvetreznik, J., Tomassi, S., Gentz, S., Hoffmann, J.E., Müller, M.P., Koch, O., Dehmelt, L., Rauh, D. Design and synthesis of Nrf2-derived hydrocarbon stapled peptides for the disruption of protein-DNA-interactions. PLoS One. 2022, 17(6), e0267651. https://doi.org/10.1371/journal.pone.0267651
47. Marchetti, G.M., Füsser, F., Singh, R.K., Brummel, M., Koch, O., Kümmel, D.*, Hippler, M.*, Structural analysis revealed a novel conformation of the NTRC reductase domain from Chlamydomonas reinhardtii. J. Struct. Biol., 2021, 214, 107829. https://doi.org/10.1016/j.jsb.2021.107829
46. Patberg, M., Isaak, A., Füsser, F., Ortiz Zacarías, N.V., Vinnenberg, L. Schulte, J., Michettia, L., Grey, L., Van der Horst, C:, Hundehege, P., Koch, O., Heitman, L.H., Budde, T., Junker, A. Piperazine squaric acid diamides, a novel class of allosteric P2X7 receptor antagonists. Eur. J. Med. Chem., 2021; 226, 113838. https://doi.org/10.1016/j.ejmech.2021.113838
45. Menke, J., Massa, J., Koch, O.* Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks. Comput. Struct. Biotechnol. J., 2021; 19, 4593-4602. https://doi.org/10.1016/j.csbj.2021.07.032
44. Humbeck, L., Pretzel, J., Spitzer, S., Koch, O.* Discovery of an Unexpected Similarity in Ligand Binding Between BRD4 and PPARγ. ACS Chem. Bio. 2021; 16(7), 1255-1265. https://doi.org/10.1021/acschembio.1c00323
43. Todesca, L.M., Maskri, S., Brömmel, K., Thale, I., Wünsch, B., Koch, O., Schwab, A.* Targeting KCa3.1 channels in cancer. Cell Physiol. Biochem. 2021; 55 (S3), 131-144. https://doi.org/10.33594/000000374
42. Ilari, D., Maskri, S., Schepmann, D., Köhler, J., Daniliuc, C.G., Koch, O., Wünsch, B.* Diastereoselective synthesis of 2-azabicyclo[3.2.1]octane derivatives as conformationally restricted KOR agonists. Org. Biomol. Chem. 2021; 19, 4082-4099. https://doi.org/10.1039/D1OB00398D
41. Menke, J., Maskri, S., Koch, O.* Computational ion channel research: From the application of artificial intelligence to molecular dynamics simulations. Cell Physiol. Biochem. 2021; 55(S3), 14-45. https://doi.org/10.33594/000000336
40. Menke, J., Koch, O.* Using Domain-Specific Fingerprints Generated Through Neural Networks to Enhance Ligand-based Virtual Screening. J. Chem. Inf. Model. 2021; 61, 664–675. https://doi.org/10.1021/acs.jcim.0c01208
39. Brömmel, K., Maskri, S. Bulk, E. Pethő, Z., Rieke, M., Budde, T. Koch, O. Schwab, A., Wünsch, B. Co-staining of KCa3.1 channels in NSCLC cells with a small-molecule fluorescent probe and antibody-based indirect immunofluorescence. ChemMedChem. 2020; 15(24), 2462-2469. https://doi.org/10.1002/cmdc.202000652
38. Brömmel, K., Maskri, S., Maisuls, I., Konken, C.P., Rieke, M., Pethő, Z., Strassert, C.A., Koch, O., Schwab, A., Wünsch, B. Synthesis of Small-Molecule Fluorescent Probes for the In Vitro Imaging of Calcium-Activated Potassium Channel KCa 3.1. Angew. Chem. Int. Ed. Engl. 2020; 59(21): 8277-8284
https://doi.org/10.1002/anie.202001201
37. Brinkjost, T., Ehrt, C., Koch, O., Mutzel, P. SCOT: Rethinking the Classification of Secondary Structure Elements. Bioinformatics. 2019; 36(8): 2417-2428. https://doi.org/10.1093/bioinformatics/btz826
36. Ehrt, C., Brinkjost, T., Koch, O.* Binding site characterization - similarity, promiscuity, and druggability. Medchemcomm. 2019; 10(7): 1145-1159. Invited, part of the themed issue “new talents”. https://doi.org/10.1039/c9md00102f
35. Géraldy, M., Morgen, M., Sehr, P., Steimbach, R.R., Moi, D., Ridinger, J., Oehme, I., Witt, O., Malz, M., Nogueira, M.S., Koch, O., Gunkel, N., Miller, A.K. Selective Inhibition of Histone Deacetylase 10: Hydrogen Bonding to the Gatekeeper Residue is Implicated. J. Med. Chem. 2019; 62(9): 4426-4443. https://doi.org/10.1021/acs.jmedchem.8b01936
34. Nogueira, M.S., Koch, O.* The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction. J. Chem. Inf. Model. 2019; 59(3): 1238 - 1252. Part of the special issue “machine learning in drug discovery”. https://doi.org/10.1021/acs.jcim.8b00773
33. Ehrt, C., Brinkjost, T., Koch, O.* A Benchmark Driven Guide to Binding Site Comparison: An Exhaustive Evaluation Using Tailor-Made Datasets, PLOS Comp. Bio. 2018; 14(11), e1006483. https://doi.org/10.1371/journal.pcbi.1006483
32. Maurer, S., Buchmuller, B., Ehrt, C., Jasper, J., Koch, O., Summerer, D. Overcoming conservation in TALE-DNA interactions: a minimal repeat scaffold enables selective recognition of an oxidized 5-methylcytosine. Chem Sci. 2018; 9(36): 7247-7252. https://dx.doi.org/10.1039/c8sc01958d
31. Gieß, M., Witte, A., Jasper, J., Koch, O., Summerer, D. Complete, Programmable Decoding of Oxidized 5-Methylcytosine Nucleobases in DNA by Chemoselective Blockage of Universal Transcription-Activator-Like Effector Repeats. J. Am. Chem. Soc. 2018; 140(18), 5904-5908. ttps://dx.doi.org/10.1021/jacs.8b02909
30. Jasper, J., Humbeck, L., Brinkjost, T., Koch, O.* A novel interaction fingerprint derived from per atom score contributions: Exhaustive evaluation of interaction fingerprint performance in docking based virtual screening, J. Cheminf. 2018; 10:15. https://dx.doi.org/10.1186/s13321-018-0264-0
29. Humbeck, L., Schäfer, T., Mutzel, P., Koch, O.* CHIPMUNK: A virtual synthesizable small molecule library for medicinal chemistry exploitable for protein-protein interaction modulators. ChemMedChem. 2018; 13, 532 –539, very important paper and cover feature. Part of the special issue “cheminformatics in drug discovery”. http://dx.doi.org/10.1002/cmdc.201700689
28. Krüger, D. M., Glas, A., Bier, D., Pospiech, N., Wallraven, K., Dietrich, L., Ottmann, C., Koch, O.*, Hennig, S.*, Grossmann, T. N.* Structure-Based Design of Non-natural Macrocyclic Peptides That Inhibit Protein–Protein Interactions, J. Med. Chem. 2017; 60(21): 8982-8988. http://dx.doi.org/10.1021/acs.jmedchem.7b01221
27. Kaitsiotou, H., Keul, M., Hardick, J., Mühlenberg, T., Ketzer, J., Ehrt, C., Krüll, J., Medda, F., Koch, O., Giordanetto, F., Bauer, S.*, Rauh, D.* Inhibitors to overcome secondary mutations in the stem cell factor receptor KIT. J. Med. Chem. 2017; 60(21): 8801-8815. ttp://dx.doi.org/10.1021/acs.jmedchem.7b00841
26. Flade,S., Jasper, J., Juhasz, M., Dankers, A., Kubik, G., Koch, O.*, Weinhold,E.*, Summerer, D.* The N6-Position of Adenine is a blind spot for TAL-effectors that enables effective binding of methylated and fluorophore-labeled DNA. ACS Chem. Biol. 2017; 12(7): 1719-1725. http://dx.doi.org/10.1021/acschembio.7b00324
25. Patel, H., Brinkjost, T., Koch, O.* PyGOLD: A python based API for docking based virtual screening workflow generation, Bioinformatics. 2017; 33(16): 2589-2590. http://dx.doi.org/10.1093/bioinformatics/btx197
24 Mejuch, T., Garivet, G., Hofer, W., Kaiser, N., Fansa, E.K., Ehrt, C., Koch, O., Baumann, M., Ziegler, S., Wittinghofer, A., Waldmann, H. Small-Molecule Inhibition of the UNC119-Cargo Interaction. Angew. Chem. Int. Ed. Engl. 2017; 56(22): 6181-6186. http://dx.doi.org/10.1002/anie.201701905
23. Schäfer, T.§, Kriege, N.§, Humbeck, L.§, Klein, K., Koch, O.*, Mutzel, P.* Scaffold Hunter: An evolving visual analytics framework for drug discovery, J. Cheminf. 2017; 9: 28. http://dx.doi.org/10.1186/s13321-017-0213-3
22. Walter, A., Helmer, R., Loaëc, N., Preu, L., Meijer, L., Kunick, C., Koch, O.* Identification of CLK1 inhibitors by a fragment-linking based virtual screening, Mol. Inf. 2017; 36(4): 1600123. http://dx.doi.org/10.1002/minf.201600123
21. Voss, S., Krüger, D.M., Koch, O., Wu, Y.W.* Spatiotemporal imaging of small GTPases activity in live cells. Proc. Natl. Acad. Sci. USA. 2016; 113(50): 14348-14353. http://dx.doi.org/10.1073/pnas.1613999113
20. Maurer, S., Giess, M., Koch, O., Summerer, D.* Interrogating Key Positions of Size-Reduced TALE Repeats Reveals a Programmable Sensor of 5-Carboxylcytosin. ACS Chem. Biol. 2016; 11(12): 3294-3299. http://dx.doi.org/10.1021/acschembio.6b00627
19 Humbeck, L., Koch, O* What can we learn from bioactivity data? Cheminformatics tools and applications in chemical biology research, ACS Chem. Biol. 2017; 12(1): 23-35, Review. http://dx.doi.org/10.1021/acschembio.6b00706
18. Zhao, L., Ehrt, C., Koch, O., Wu, Y.W.* One-Pot N2C/C2C/N2N Ligation to Trap Weak Protein-Protein Interactions. Angew. Chem. Int. Ed. Engl. 2016; 55(28): 8129-8133. http://dx.doi.org/10.1002/anie.201601299
17. Ehrt, C., Brinkjost, T., Koch, O.* The Impact of Binding Site Comparisons on Rational Drug Design. J. Med. Chem. 2016; 59: 4121-4151, Perspective. Part of the special issue “computational methods for medicinal chemistry”. http://dx.doi.org/10.1021/acs.jmedchem.6b00078
16. Orban, O.C., Korn, R.S., Benítez, D., Medeiros, A., Preu, L., Loaëc, N., Meijer, L., Koch, O., Comini, M.A.*, Kunick, C.* 5-Substituted 3-chlorokenpaullone derivatives are potent inhibitors of Trypanosoma brucei bloodstream forms. Bioorg. Med. Chem. 2016; 24(16): 3790-800. http://dx.doi.org/10.1016/j.bmc.2016.06.023
15. Terwege, T., Hanekamp, W., Garzinsky, D., König, S., Koch, O. & Lehr, M. ω-Imidazolyl- and ω-Tetrazolylalkylcarbamates as Inhibitors of Fatty Acid Amide Hydrolase: Biological Activity and in vitro Metabolic Stability. ChemMedChem. 2016; 11: 429-443. http://dx.doi.org/10.1002/cmdc.201500445
14. Pelay-Gimeno, M., Glas, A., Koch, O. & Grossmann, T.N.* Structure-based design of inhibitors of protein‒protein interactions: Mimicking peptide-binding epitope. Angew. Chem. Int. Ed. 2015; 54: 8896-8927, Review. http://dx.doi.org/10.1002/anie.201412070
13. Längle, D., Marquadt, V., Heider, E., Vigante, B., Duburs, G., Luntena, I., Flötgen, D., Golz, C., Strohmann, C., Koch, O., Schade, D.* Design, synthesis and 3D-QSAR studies of novel 1,4-dihydropyridines as TGFβ/Smad inhibitors. Eur. J. Med. Chem. 2015; 95: 249-266. http://dx.doi.org/10.1016/j.ejmech.2015.03.027
12. Maiwald, F., Benítez, D., Charquero, D., Dar, M.A., Erdmann, H., Preu, L., Koch, O., Hölscher, C., Loaëc, N., Meijer, L., Comini, M.A.*, Kunick, C.* 9- and 11-substituted 4-azapaullones are potent and selective inhibitors of African trypanosoma. Eur. J. Med. Chem. 2014; 83: 274-283. http://dx.doi.org/10.1016/j.ejmech.2014.06.020
11. Schade, D.*, Kotthaus, J., Riebling, L., Kotthaus, J., Müller-Fielitz, H., Raasch, W., Koch, O., Seidel, N., Schmidtke, M., Clement, B. Development of novel potent orally bioavailable oseltamivir derivatives active against resistant influenza A. J. Med. Chem. 2014; 57(3): 759-769. http://dx.doi.org/10.1021/jm401492x
10. Klein, K., Koch, O., Kriege, N.*, Mutzel, P., Schäfer, T. Visual Analysis of Biological Activity Data with Scaffold Hunter. Mol. Inf. 2013; 32 (11-12): 964-975, invited, authors in alphabetic order. http://dx.doi.org/10.1002/minf.201300087
9. Koch, O.*, Jäger, T., Heller, K., Khandavalli, P.C., Pretzel, J., Becker, K., Flohé, L., Selzer, P.M.* Identification of M. tuberculosis Thioredoxin Reductase Inhibitors Based on High-Throughput Docking Using Constraints. J. Med. Chem. 2013; 56(12): 4849-4859. http://dx.doi.org/10.1021/jm3015734
8. Koch, O.*, Cappel, D., Nocker, M., Jäger, T., Flohé, L., Sotriffer, C.A., Selzer, P.M.* Molecular dynamics reveal binding mode of glutathionylspermidine by trypanothione synthetase. PLoS One. 2013; 8(2): e56788. http://dx.doi.org/10.1371/journal.pone.0056788
7. Koch, O.* Advances in the prediction of turn structures in peptides and proteins, Molecular Informatics. 2012; 31(9): 624-630, invited. http://dx.doi.org/10.1002/minf.201200021
6. Koch, O.*: The Use of Secondary Structure Element Information in Drug Design: Polypharmacology and Conserved Motifs in Protein-Ligand Binding and Protein-Protein Interfaces. Future Medicinal Chemistry. 2011; 3(6): 699-708, invited. http://dx.doi.org/10.4155/fmc.11.26
5. Koch, O.*, Cole, J.*: An automated method for consistent helix assignment using turn information. Proteins. 2011; 79(5): 1416-1426. http://dx.doi.org/10.1002/prot.22968
4. Koch, O., Cole, J., Block, P., Klebe, G.*: Secbase: database module to retrieve secondary structure elements with ligand binding motifs. J. Chem. Inf. Model. 2009; 49(10): 2388-2402. http://dx.doi.org/10.1021/ci900202d
3. Koch, O., Klebe, G.*: Turns revisited: a uniform and comprehensive classification of normal, open, and reverse turn families minimizing unassigned random chain portions. Proteins. 2009; 74(2): 353-367. http://dx.doi.org/10.1002/prot.22185
2. Meissner, M., Koch, O., Klebe, G., Schneider, G.*: Prediction of turn types in protein structure by machine-learning classifiers. Proteins. 2009; 74(2): 344-352. http://dx.doi.org/10.1002/prot.22164
1. Koch, O., Bocola, M., Klebe, G.*: Cooperative effects in hydrogen-bonding of protein secondary structure elements: a systematic analysis of crystal data using Secbase. Proteins. 2005; 61(2): 310-317. http://dx.doi.org/10.1002/prot.20613