Computational Pharmacy (Lill)

Eberhardt, J. et al. (2024) ‘Combining Bayesian optimization with sequence- or structure-based strategies for optimization of peptide-binding protein’, ChemRxiv [Preprint]. American Chemical Society (ACS). Available at: https://doi.org/10.26434/chemrxiv-2023-b7l81-v2.

Diamond, Justin and Lill, Markus A. (2024) ‘Neural SHAKE: Geometric Constraints in Graph Generative Models’, pp. 43–57. Available at: https://doi.org/10.1007/978-3-031-72359-9_4.

Höing, Lars et al. (2024) ‘Biosynthesis of the bacterial antibiotic 3,7-dihydroxytropolone through enzymatic salvaging of catabolic shunt products’, Chemical Science, 15(20), pp. 7749–7756. Available at: https://doi.org/10.1039/d4sc01715c.

Kędzierski, Jacek et al. (2024) ‘In silico and in vitro assessment of drugs potentially causing adverse effects by inhibiting CYP17A1’, Toxicology and Applied Pharmacology, 486. Available at: https://doi.org/10.1016/j.taap.2024.116945.

Reinhardt, Jakob K. et al. (2024) ‘Vitex agnus castus Extract Ze 440: Diterpene and Triterpene’s Interactions with Dopamine D2 Receptor’, International Journal of Molecular Sciences, 25. Available at: https://doi.org/10.3390/ijms252111456.

Schäfer, Anima M. et al. (2024) ‘St. John’s Wort Formulations Induce Rat CYP3A23-3A1 Independent of Their Hyperforin Content’, Molecular Pharmacology, 105, pp. 14–22. Available at: https://doi.org/10.1124/molpharm.123.000725.

Varga, Norbert et al. (2024) ‘Strengthening an Intramolecular Non-Classical Hydrogen Bond to Get in Shape for Binding’, Angewandte Chemie - International Edition, 63. Available at: https://doi.org/10.1002/anie.202406024.

Sellner, M.S., Lill, M.A. and Smieško, M. (2023) ‘PanScreen: A Comprehensive Approach to Off-Target Liability Assessment’. Cold Spring Harbor Laboratory. Available at: https://doi.org/10.1101/2023.11.16.567496.

Sellner, M.S., Lill, M.A. and Smieško, M. (2023) ‘Quality Matters: Deep Learning-Based Analysis of Protein-Ligand Interactions with Focus on Avoiding Bias’. Cold Spring Harbor Laboratory. Available at: https://doi.org/10.1101/2023.11.13.566916.

Eberhardt, Jérôme et al. (2023) ‘Combining Bayesian optimization with sequence- or structure-based strategies for optimization of peptide-binding protein’, ChemRxiv [Preprint]. American Chemical Society (ACS). Available at: https://doi.org/10.26434/chemrxiv-2023-b7l81.

Damilakis, Emmanouil et al. (2023) ‘Assessing prescription of antibiotics after vaccination against pneumococcal pneumonia; using prescription sequence symmetry analysis’, Clinical Microbiology and Infection, null. Available at: https://doi.org/10.1016/j.cmi.2023.10.003.

Hinz, Florian B, Mahmoud, Amr H and Lill, Markus A (2023) ‘Prediction of molecular field points using SE(3)-transformer model’, Machine Learning: Science and Technology, 4. Available at: https://doi.org/10.1088/2632-2153/ace67b.

Jäger, Marie-Christin et al. (2023) ‘Virtual screening and biological evaluation to identify pharmaceuticals potentially causing hypertension and hypokalemia by inhibiting steroid 11β-hydroxylase’, Toxicology and Applied Pharmacology, 475. Available at: https://doi.org/10.1016/j.taap.2023.116638.

Kędzierski, Jacek et al. (2023) ‘Assessment of the inhibitory potential of anabolic steroids towards human AKR1D1 by computational methods and in vitro evaluation’, Toxicology Letters, 384, pp. 1–13. Available at: https://doi.org/10.1016/j.toxlet.2023.07.006.

Kolesnyk, Serhii et al. (2023) ‘A battery of in silico models application for pesticides exerting reproductive health effects: Assessment of performance and prioritization of mechanistic studies’, Toxicology in Vitro, 93. Available at: https://doi.org/10.1016/j.tiv.2023.105706.

Masters, Matthew R et al. (2023) ‘Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility.’, Journal of chemical information and modeling, 63(6), pp. 1695–1707. Available at: https://doi.org/10.1021/acs.jcim.2c01436.

Schreier, Verena N. et al. (2023) ‘Evaluating the food safety and risk assessment evidence-base of polyethylene terephthalate oligomers: A systematic evidence map’, Environment international, 176, p. 107978. Available at: https://doi.org/10.1016/j.envint.2023.107978.

Sellner, Manuel S., Mahmoud, Amr H. and Lill, Markus A. (2023) ‘Efficient virtual high-content screening using a distance-aware transformer model’, Journal of Cheminformatics, 15. Available at: https://doi.org/10.1186/s13321-023-00686-z.

Wagner, B. et al. (2023) ‘A Structural-Reporter Group to Determine the Core Conformation of Sialyl Lewis^x Mimetics’, Molecules, 28. Available at: https://doi.org/10.3390/molecules28062595.

Wehrli, Lydia et al. (2023) ‘The action of physiological and synthetic steroids on the calcium channel CatSper in human sperm’, Frontiers in Cell and Developmental Biology, 11. Available at: https://doi.org/10.3389/fcell.2023.1221578.

Winker, Moritz et al. (2023) ‘Immunological evaluation of herbal extracts commonly used for treatment of mental diseases during pregnancy’, Scientific Reports, 13(1), p. 9630. Available at: https://doi.org/10.1038/s41598-023-35952-5.

Anyanwu, Robin (2022) Predicting the Solubility of Collection-11 Inhibitors with in silico ADME Predictors. . Translated by Smiesko Martin. Masterarbeit.

Dürr, L et al. (2022) ‘Dimerosesquiterpene and Sesquiterpene Lactones from Artemisia argyi Inhibiting Oncogenic PI3K/AKT Signaling in Melanoma Cells’, Journal of Natural Product, 85, pp. 2557–2569. Available at: https://doi.org/10.1021/acs.jnatprod.2c00471.

Fischer, André et al. (2022) ‘Ligand pathways in estrogen-related receptors.’, Journal of biomolecular structure & dynamics, pp. 1–10. Available at: https://doi.org/10.1080/07391102.2022.2027818.

Hinz, Florian B., Mahmoud, Amr H. and Lill, Markus A. (2022) ‘Prediction of Molecular Field Points using SE (3)-Transformer Model’. ICLR2022 Machine Learning for Drug Discovery: ICLR2022 Machine Learning for Drug Discovery.

Inderbinen, Silvia G et al. (2022) ‘Activation of retinoic acid-related orphan receptor γ(t) by parabens and benzophenone UV-filters.’, Toxicology, 471, p. 153159. Available at: https://doi.org/10.1016/j.tox.2022.153159.

Lamers, Christina et al. (2022) ‘Insight into mode-of-action and structural determinants of the compstatin family of clinical complement inhibitors.’, Nature communications, 13(1), p. 5519. Available at: https://doi.org/10.1038/s41467-022-33003-7.

Mahmoud, Amr H et al. (2022) ‘Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation.’, Journal of chemical information and modeling, 62(7), pp. 1602–1617. Available at: https://doi.org/10.1021/acs.jcim.1c01438.

Masters, Matthew R. et al. (2022) ‘Deep learning model for flexible and efficient protein-ligand docking’. ICLR2022 Machine Learning for Drug Discovery: ICLR2022 Machine Learning for Drug Discovery.

Messmer, Livius (2022) The generation and refinement of high-quality 3D structures for various pharmaceutically & toxicologically relevant targets and ligands. . Translated by Smiesko Martin. Masterarbeit.

Papaj, Katarzyna et al. (2022) ‘Evaluation of Xa inhibitors as potential inhibitors of the SARS-CoV-2 Mpro protease.’, PloS one, 17(1), p. e0262482. Available at: https://doi.org/10.1371/journal.pone.0262482.

Schreier, Verena N. et al. (2022) ‘Evaluating the food safety and risk assessment evidence-base of polyethylene terephthalate oligomers: Protocol for a systematic evidence map’, Environment International, 167, p. 107387. Available at: https://doi.org/10.1016/j.envint.2022.107387.

Sellner, Manuel S., Mahmoud, Amr H. and Lill, Markus A. (2022) ‘High-Content Similarity-Based Virtual Screening Using a Distance Aware Transformer Model’. ICLR2022 Machine Learning for Drug Discovery: ICLR2022 Machine Learning for Drug Discovery.

Yao Wei (2022) DEEP LEARNING APPROACHES FOR COARSE-GRAINED PROTEIN MODELS. . Translated by Lill Markus A. Masterarbeit.

Aschwanden, Roman (2021) Computational Exploration of Ligand Tunnels and Ligand Transfer in Protein-Protein Complexes Involved int the Retinoid Pathway. . Translated by Smiesko Martin; Fischer André. Masterarbeit.

Bardakci, Ferhat (2021) Small Molecule Binding Pathways to Nuclear Receptors (Estorgen-related receptor). . Translated by Smiesko Martin; Fischer André. Masterarbeit.

Fischer, André (2021) Ligand Recognition and Specificity of Metabolic Enzymes and Nuclear Receptors. . Translated by Smiesko Martin; Ricklin Daniel. Dissertation.

Fischer, André et al. (2021) ‘Conformational Changes of Thyroid Receptors in Response to Antagonists.’, Journal of chemical information and modeling, 61(2), pp. 1010–1019. Available at: https://doi.org/10.1021/acs.jcim.0c01403.

Fischer, André et al. (2021) ‘Computational Assessment of Combination Therapy of Androgen Receptor-Targeting Compounds.’, Journal of chemical information and modeling, 61(2), pp. 1001–1009. Available at: https://doi.org/10.1021/acs.jcim.0c01194.

Fischer, André et al. (2021) ‘Computational Selectivity Assessment of Protease Inhibitors against SARS-CoV-2.’, International journal of molecular sciences, 22(4), p. 2065. Available at: https://doi.org/10.3390/ijms22042065.

Fischer, André et al. (2021) ‘Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results.’, Journal of medicinal chemistry, 64(5), pp. 2489–2500. Available at: https://doi.org/10.1021/acs.jmedchem.0c02227.

Fischer, André and Smieško, Martin (2021) ‘A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment’, International Journal of Molecular Sciences, 22(24), p. 13215. Available at: https://doi.org/10.3390/ijms222413215.

Inderbinen, Silvia G. et al. (2021) ‘Species-specific differences in the inhibition of 11β-hydroxysteroid dehydrogenase 2 by itraconazole and posaconazole’, Toxicology and applied pharmacology, 412, p. 115387. Available at: https://doi.org/10.1016/j.taap.2020.115387.

Papaj, Katarzyna et al. (2021) ‘Investigation of Thiocarbamates as Potential Inhibitors of the SARS-CoV-2 Mpro’, Pharmaceuticals (Basel, Switzerland), 14(11), p. 1153. Available at: https://doi.org/10.3390/ph14111153.

Ruggli, Maria (2021) In silico slectivity assessment of collectin-11 antagonists. . Translated by Smiesko Martin; Ricklin Daniel. Masterarbeit.

Sellner, Manuel et al. (2021) ‘Conformational Landscape of Cytochrome P450 Reductase Interactions’, International journal of molecular sciences, 22(3), p. 1023. Available at: https://doi.org/10.3390/ijms22031023.

Fischer, A. et al. (2020) Potential Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 606 Million Compounds. American Chemical Society (ACS). Available at: https://doi.org/10.26434/chemrxiv.11923239.v2.

Charleen Don (2020) Pharmacogenetic modeling of human cytochrome P450 2D6 - On the force of variation in inducing toxicity. . Translated by Smiesko Martin; Ricklin Daniel. Dissertation.

Don, Charleen G. and Smieško, Martin (2020) ‘In Silico Pharmacogenetics CYP2D6 Study Focused on the Pharmacovigilance of Herbal Antidepressants’, Frontiers in Pharmacology, 11, p. 683. Available at: https://doi.org/10.3389/fphar.2020.00683.

Don, Charleen G. and Smieško, Martin (2020) ‘Deciphering Reaction Determinants of Altered-Activity CYP2D6 Variants by Well-Tempered Metadynamics Simulation and QM/MM Calculations’, Journal of Chemical Information and Modeling, 60(12), pp. 6642–6653. Available at: https://doi.org/10.1021/acs.jcim.0c01091.

Fischer, André et al. (2020) ‘Potential Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 606 Million Compounds’, International journal of molecular sciences, 21(10), p. 3626. Available at: https://doi.org/10.3390/ijms21103626.

Fischer, André and Smieško, Martin (2020) ‘Allosteric Binding Sites On Nuclear Receptors: Focus On Drug Efficacy and Selectivity’, International journal of molecular sciences, 21(2), p. 534. Available at: https://doi.org/10.3390/ijms21020534.

Frehner, Gabriela (2020) Conformational Changes of Thyroid Receptors Due to Antagonist Binding. . Translated by Smiesko Martin. Masterarbeit.

Ghanbarpour, Ahmadreza and Lill, Markus A. (2020) ‘Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks’. arXiv (15900). Available at: https://arxiv.org/abs/2010.15900.

Ghanbarpour, Ahmadreza, Mahmoud, Amr H. and Lill, Markus A. (2020) ‘Instantaneous generation of protein hydration properties from static structures’, Communications Chemistry, 3, p. 188. Available at: https://doi.org/10.1038/s42004-020-00435-5.

Häuptli, Florian (2020) Polypharmacy at the androgen receptor-modeling drug-drug interactions. . Translated by Smiesko Martin. Masterarbeit.

Mahmoud, Amr H., Lill, Jonas F. and Lill, Markus A. (2020) ‘Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix’. arXiv (12027). Available at: https://arxiv.org/abs/2008.12027.

Mahmoud, Amr H. et al. (2020) ‘Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning’, Communications Chemistry, 3, p. 19. Available at: https://doi.org/10.1038/s42004-020-0261-x.

Mahmoud, Amr H et al. (2020) ‘Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning’, Communications Chemistry. Nature Publishing Group (Communications Chemistry, 19). Available at: https://doi.org/10.1038/s42004-020-0261-x.

Mahmoud, Amr, Lill, Jonas F. and Lill, Markus A. (2020) ‘Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix’, Quantitative Biology, Biomolecules [Preprint]. arXiv (Quantitative Biology, Biomolecules). Available at: https://doi.org/arxiv:2008.12027.

Neranjan, Santhosh (2020) Large-scale virtual screening of natural compounds in OTC preparations. . Translated by Smiesko Martin. Masterarbeit.

Reinhardt, Jakob K. et al. (2020) ‘Compounds from Toddalia asiatica: Immunosuppressant activity and absolute configurations’, Journal of Natural Products, 83(10), pp. 3012–3020. Available at: https://doi.org/10.1021/acs.jnatprod.0c00564.

Mahmoud, A., Yang, Y. and Lill, M. (2019) Improving Atom Type Diversity and Sampling in Co-Solvent Simulations Using λ-Dynamics. American Chemical Society (ACS). Available at: https://doi.org/10.26434/chemrxiv.7557224.v1.

Bartolowits, Matthew D. et al. (2019) ‘Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen’, ACS omega, 4(12), pp. 15181–15196. Available at: https://doi.org/10.1021/acsomega.9b02079.

Cassell, Robert J. et al. (2019) ‘Rubiscolins are naturally occurring G protein-biased delta opioid receptor peptides’, European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology, 29(3), pp. 450–456. Available at: https://doi.org/10.1016/j.euroneuro.2018.12.013.

Fischer, André and Smieško, Martin (2019) ‘Spontaneous Ligand Access Events to Membrane-Bound Cytochrome P450 2D6 Sampled at Atomic Resolution’, Scientific reports, 9(1), p. 16411. Available at: https://doi.org/10.1038/s41598-019-52681-w.

Fischer, André and Smieško, Martin (2019) ‘Ligand Pathways in Nuclear Receptors’, Journal of chemical information and modeling, 59(7), pp. 3100–3109. Available at: https://doi.org/10.1021/acs.jcim.9b00360.

Joël Wahl (2019) Quantifying the Role of Water in Ligand-­Protein Binding Processes. . Translated by Vedani Angelo; Smiesko Martin. Dissertation.

Kaur, Jatinder et al. (2019) ‘Optimization of a 1,3,4-oxadiazole series for inhibition of Ca; 2+; /calmodulin-stimulated activity of adenylyl cyclases 1 and 8 for the treatment of chronic pain’, European journal of medicinal chemistry, 162, pp. 568–585. Available at: https://doi.org/10.1016/j.ejmech.2018.11.036.

Mahmoud, Amr H., Yang, Ying and Lill, Markus A. (2019) ‘Improving Atom-Type Diversity and Sampling in Cosolvent Simulations Using λ-Dynamics’, Journal of chemical theory and computation, 15(5), pp. 3272–3287. Available at: https://doi.org/10.1021/acs.jctc.8b00940.

Mahmoud, Amr et al. (2019) ‘Elucidating the Multiple Roles of Hydration in Protein-Ligand Binding via Layerwise Relevance Propagation and Big Data Analytics’, Biological and Medicinal Chemistry [Preprint]. ChemRxiv (Biological and Medicinal Chemistry). Available at: https://doi.org/10.26434/chemrxiv.7723223.v1.

Moawad, Sherif (2019) Dukkah. . Translated by Smiesko Martin. Masterarbeit.

Roel-Touris, Jorge et al. (2019) ‘Less is more: Coarse-grained integrative modeling of large biomolecular assemblies with HADDOCK’, Journal of chemical theory and computation, 15(11), pp. 6358–6367. Available at: https://doi.org/10.1021/acs.jctc.9b00310.

Sellner, Manuel (2019) Conformational Flexibility of the Cytochrome P450 Reductase and Its interplay with CYP2D6. . Translated by Smiesko Martin; Don Charleen Georgette; Fischer André. Masterarbeit.

Yang, Ying, Mahmoud, Amr H. and Lill, Markus A. (2019) ‘Modeling of Halogen-Protein Interactions in Co-Solvent Molecular Dynamics Simulations’, Journal of chemical information and modeling, 59(1), pp. 38–42. Available at: https://doi.org/10.1021/acs.jcim.8b00806.

Reinhardt, J. et al. (2017) ‘Absolute configuration of sesquiterpene lactones with potent immunosuppressive activity’. Georg Thieme Verlag KG. Available at: https://doi.org/10.1055/s-0037-1608281.

Guccione, Clizia et al. (2017) ‘Corrigendum to “Andrographolide-loaded nanoparticles for brain delivery: Formulation, characterisation and in vitro permeability using hCMEC/D3 cell line” (Eur. J. Pharm. Biopharm. (2017) 119 (253–263) (S0939641117301613) (10.1016/j.ejpb.2017.06.018))’, European Journal of Pharmaceutics and Biopharmaceutics, 120, p. 146. Available at: https://doi.org/10.1016/j.ejpb.2017.09.010.

Pang, Lijuan et al. (2017) ‘FimH antagonists - Solubility: Vs. permeability’, Carbohydrate Chemistry, 42, pp. 248–273. Available at: https://doi.org/10.1039/9781782626657-00248.

Hamburger, M. et al. (2015) ‘Pharmacokinetic, in vitro and in silico assessment of anti-inflammatory alkaloids from Isatis tinctoria L.’, Planta Medica, 81(11). Available at: https://doi.org/10.1055/s-0035-1556303.

Kleeb, S. et al. (2015) ‘Crystal structure of FimH in complex with 3′-Chloro-4′-(alpha-D-mannopyranosyloxy)-biphenyl-4-carbonitrile’. Worldwide Protein Data Bank. Available at: https://doi.org/10.2210/pdb4cst/pdb.

Kleeb, S. et al. (2015) ‘Crystal structure of FimH in complex with a sulfonamide biphenyl alpha D-mannoside’. Worldwide Protein Data Bank. Available at: https://doi.org/10.2210/pdb4css/pdb.

Vedani, A. and Smiesko, M. (2013) ‘VirtualToxLab – insilico prediction if the toxic potential of drugs and chemicals’, Toxicology Letters, 221, p. S52. Available at: https://doi.org/10.1016/j.toxlet.2013.06.225.

Ebrahimi, S. et al. (2012) ‘Bisabololoxide derivatives from Artemisia persica, and determination of their absolute configurations by ECD’, Planta Medica, 78(11). Available at: https://doi.org/10.1055/s-0032-1321289.

Vedani, Angelo and Smiesko, Martin (2009) ‘In silico toxicology in drug discovery - Concepts based on three-dimensional models’, Alternatives to Laboratory Animals, 37, pp. 477–496. Available at: https://doi.org/10.1177/026119290903700506.

Vedani, Angelo et al. (2009) ‘VirtualToxLab™ - In silico prediction of the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products. Two years and 2,000 compounds of experience: A progress report’, Altex, 26, pp. 167–176. Available at: https://doi.org/10.14573/altex.2009.3.167.

Sramko, M., Smiesko, M. and Remko, M. (2008) ‘Accurate aqueous proton dissociation constants calculations for selected angiotensin-converting enzyme inhibitors’, Journal of Biomolecular Structure and Dynamics, 25(6), pp. 599–608. Available at: https://doi.org/10.1080/07391102.2008.10507206.

Vedani, A. et al. (2008) ‘VirtualToxLab - In silico prediction of the endocrine-disrupting potential of drugs and chemicals’, Chimia, 62(5), pp. 322–328. Available at: https://doi.org/10.2533/chimia.2008.322.

Vedani, Angelo and Smiesko, Martin (2008) ‘Structure-Based computational pharmacology and toxicology’, pp. 549–572. Available at: https://doi.org/10.1142/9789812778789_0020.

Gao, Ganpan et al. (2007) ‘Mimetics of the tri- and tetrasaccharide epitope of GQ1bα as myelin-associated glycoprotein (MAG) ligands’, Bioorganic and Medicinal Chemistry, 15, pp. 7459–7469. Available at: https://doi.org/10.1016/j.bmc.2007.07.033.

Vedani, Angelo et al. (2007) ‘VirtualToxLab - In silico prediction of the toxic potential of drugs and environmental chemicals: Evaluation status and internet access protocol’, Altex, 24, pp. 153–161.