Computational Pharmacy (Lill)

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(21). Available at: https://doi.org/10.3390/ijms252111456.

Masters, Matthew R., Mahmoud, Amr H. and Lill, Markus A. (2024) ‘Parallel Sampling of Protein-Ligand Dynamics’. Cold Spring Harbor Laboratory. Available at: https://doi.org/10.1101/2024.07.08.602465.

Masters, Matthew R., Mahmoud, Amr H. and Lill, Markus A. (2024) ‘Do Deep Learning Models for Co-Folding Learn the Physics of Protein-Ligand Interactions?’ Cold Spring Harbor Laboratory. Available at: https://doi.org/10.1101/2024.06.03.597219.

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.

Damilakis, Emmanouil et al. (2024) ‘Assessing prescription of antibiotics after vaccination against pneumococcal pneumonia; using prescription sequence symmetry analysis’, Clinical Microbiology and Infection, 30(3), pp. 375–379. Available at: https://doi.org/10.1016/j.cmi.2023.10.003.

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

Schäfer, Anima M. et al. (2024) ‘St. John’s Wort Formulations Induce Rat CYP3A23-3A1 Independent of Their Hyperforin ContentS’, Molecular Pharmacology, 105(1), pp. 14–22. Available at: https://doi.org/10.1124/molpharm.123.000725.

Diamond, Justin and Lill, Markus A. (2024) ‘Neural SHAKE: Geometric Constraints in Graph Generative Models’, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)), pp. 43–57. Available at: https://doi.org/10.1007/978-3-031-72359-9_4.

Hinz, Florian B. et al. (2024) ‘Accelerated Hydration Site Localization and Thermodynamic Profiling’. Available at: https://doi.org/https://doi.org/10.48550/arXiv.2411.15618.

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.

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.

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(1). Available at: https://doi.org/10.1186/s13321-023-00686-z.

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.

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.

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(3). Available at: https://doi.org/10.1088/2632-2153/ace67b.

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.

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.

Wagner, B. et al. (2023) ‘A Structural-Reporter Group to Determine the Core Conformation of Sialyl Lewisx Mimetics’, Molecules, 28(6). Available at: https://doi.org/10.3390/molecules28062595.

Fischer, André et al. (2023) ‘Ligand pathways in estrogen-related receptors’, Journal of Biomolecular Structure and Dynamics, 41(5), pp. 1639–1648. Available at: https://doi.org/10.1080/07391102.2022.2027818.

Lee, S.J., Mahmoud, A.H. and Lill, M.A. (2023) ‘ACCURATE FREE ENERGY ESTIMATIONS OF MOLECULAR SYSTEMS VIA FLOW-BASED TARGETED FREE ENERGY PERTURBATION’. Available at: https://doi.org/10.48550/arxiv.2302.11855.

Masters, Matthew R., Mahmoud, Amr H. and Lill, Markus A. (2023) ‘POCKETNET: LIGAND-GUIDED POCKET PREDICTION FOR BLIND DOCKING’, in CLR 2023-Machine Learning for Drug Discovery workshop. CLR 2023-Machine Learning for Drug Discovery workshop: ICLR 2023 (CLR 2023-Machine Learning for Drug Discovery workshop).

Masters, Matthew R., Mahmoud, Amr H. and Lill, Markus A. (2023) ‘FusionDock: Physics-informed Diffusion Model for Molecular Docking’, in ICML2023 CompBio Workshop. ICML2023 CompBio Workshop (ICML2023 CompBio Workshop).

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.

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.

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.

Dürr, L et al. (2022) ‘A Dimerosesquiterpene and Sesquiterpene Lactones from Artemisia argyi Inhibiting Oncogenic PI3K/AKT Signaling in Melanoma Cells’, Journal of Natural Products, 85(11), pp. 2557–2569. Available at: https://doi.org/10.1021/acs.jnatprod.2c00471.

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.

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.

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

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.

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. Masterarbeit.

Papaj, Katarzyna et al. (2022) ‘Evaluation of Xa inhibitors as potential inhibitors of the SARS-CoV-2 Mpro protease’, PLoS ONE, 17(1 January), 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. Masterarbeit.

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 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), pp. 1–17. Available at: https://doi.org/10.3390/ijms22042065.

Aschwanden, Roman (2021) Computational Exploration of Ligand Tunnels and Ligand Transfer in Protein-Protein Complexes Involved int the Retinoid Pathway. Masterarbeit.

Bardakci, Ferhat (2021) Small Molecule Binding Pathways to Nuclear Receptors (Estorgen-related receptor). Masterarbeit.

Fischer, André (2021) Ligand Recognition and Specificity of Metabolic Enzymes and Nuclear Receptors. Dissertation.

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. 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.

Mahmoud, Amr H et al. (2020) ‘Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning’, Communications Chemistry. Nature Research (Communications Chemistry, 1). Available at: https://doi.org/10.1038/s42004-020-0261-x.

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. 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. 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. 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, 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. 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. 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. 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. 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.