Prof. Dr. Markus A. Lill

Masters, Matthew R., Mahmoud, Amr H., & Lill, Markus A. (2024). Parallel Sampling of Protein-Ligand Dynamics [Posted-content]. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2024.07.08.602465

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

Damilakis, Emmanouil, Meier, Christoph R., Huber, Carola A., Lill, Markus, & Schneider, Cornelia. (2024). Assessing prescription of antibiotics after vaccination against pneumococcal pneumonia; using prescription sequence symmetry analysis. Clinical Microbiology and Infection, 30, 375–379. https://doi.org/10.1016/j.cmi.2023.10.003

Eberhardt, Jérôme, Lees, Aidan, Lill, Markus, & Schwede, Torsten. (2024). Combining Bayesian optimization with sequence- or structure-based strategies for optimization of peptide-binding protein [Posted-content]. In ChemRxiv. American Chemical Society (ACS). https://doi.org/10.26434/chemrxiv-2023-b7l81-v2

Diamond, Justin, & Lill, Markus A. (2024). Neural SHAKE: Geometric Constraints in Graph Generative Models. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15025 LNCS, 43–57. https://doi.org/10.1007/978-3-031-72359-9_4

Hinz, Florian B., Masters, Matthew R., Kieu, Julia N., Mahmoud, Amr H., & Lill, Markus A. (2024, January 1). Accelerated Hydration Site Localization and Thermodynamic Profiling. https://doi.org/https://doi.org/10.48550/arXiv.2411.15618

Höing, Lars, Sowa, Sven T., Toplak, Marina, Reinhardt, Jakob K., Jakob, Roman, Maier, Timm, Lill, Markus A., & Teufel, Robin. (2024). Biosynthesis of the bacterial antibiotic 3,7-dihydroxytropolone through enzymatic salvaging of catabolic shunt products [Journal-article]. Chemical Science, 15(20), 7749–7756. https://doi.org/10.1039/d4sc01715c

Sellner, Manuel S., Mahmoud, Amr H., & Lill, Markus A. (2023). Efficient virtual high-content screening using a distance-aware transformer model. Journal of Cheminformatics, 15(1). https://doi.org/10.1186/s13321-023-00686-z

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

Masters, Matthew R, Mahmoud, Amr H, Wei, Yao, & Lill, Markus A. (2023). Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility. Journal of Chemical Information and Modeling, 63(6), 1695–1707. https://doi.org/10.1021/acs.jcim.2c01436

Fischer, André, Bardakci, Ferhat, Sellner, Manuel, Lill, Markus A, & Smieško, Martin. (2023). Ligand pathways in estrogen-related receptors. Journal of Biomolecular Structure and Dynamics, 41(5), 1639–1648. https://doi.org/10.1080/07391102.2022.2027818

Lee, S.J., Mahmoud, A.H., & Lill, M.A. (2023, January 1). ACCURATE FREE ENERGY ESTIMATIONS OF MOLECULAR SYSTEMS VIA FLOW-BASED TARGETED FREE ENERGY PERTURBATION. https://doi.org/10.48550/arxiv.2302.11855

Masters, Matthew R., Mahmoud, Amr H., & Lill, Markus A. (2023, January 1). POCKETNET: LIGAND-GUIDED POCKET PREDICTION FOR BLIND DOCKING. CLR 2023-Machine Learning for Drug Discovery Workshop.

Masters, Matthew R., Mahmoud, Amr H., & Lill, Markus A. (2023, January 1). FusionDock: Physics-informed Diffusion Model for Molecular Docking. ICML2023 CompBio Workshop.

Mahmoud, Amr H, Masters, Matthew, Lee, Soo Jung, & Lill, Markus A. (2022). Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation. Journal of Chemical Information and Modeling, 62(7), 1602–1617. https://doi.org/10.1021/acs.jcim.1c01438

Hinz, Florian B., Mahmoud, Amr H., & Lill, Markus A. (2022, January 1). Prediction of Molecular Field Points using SE (3)-Transformer Model.

Masters, Matthew R., Mahmoud, Amr H., Wei, Yao, & Lill, Markus A. (2022, January 1). Deep learning model for flexible and efficient protein-ligand docking.

Papaj, Katarzyna, Spychalska, Patrycja, Kapica, Patryk, Fischer, André, Nowak, Jakub, Bzówka, Maria, Sellner, Manuel, Lill, Markus A, Smieško, Martin, & Góra, Artur. (2022). Evaluation of Xa inhibitors as potential inhibitors of the SARS-CoV-2 Mpro protease. PLoS ONE, 17(1 January), e0262482. https://doi.org/10.1371/journal.pone.0262482

Sellner, Manuel S., Mahmoud, Amr H., & Lill, Markus A. (2022, January 1). High-Content Similarity-Based Virtual Screening Using a Distance Aware Transformer Model.

Fischer, André, Smieško, Martin, Sellner, Manuel, & Lill, Markus A. (2021). Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results. Journal of Medicinal Chemistry, 64(5), 2489–2500. https://doi.org/10.1021/acs.jmedchem.0c02227

Fischer, André, Frehner, Gabriela, Lill, Markus A, & Smieško, Martin. (2021). Conformational Changes of Thyroid Receptors in Response to Antagonists. Journal of Chemical Information and Modeling, 61(2), 1010–1019. https://doi.org/10.1021/acs.jcim.0c01403

Fischer, André, Häuptli, Florian, Lill, Markus A, & Smieško, Martin. (2021). Computational Assessment of Combination Therapy of Androgen Receptor-Targeting Compounds. Journal of Chemical Information and Modeling, 61(2), 1001–1009. https://doi.org/10.1021/acs.jcim.0c01194

Fischer, André, Sellner, Manuel, Mitusińska, Karolina, Bzówka, Maria, Lill, Markus A, Góra, Artur, & Smieško, Martin. (2021). Computational selectivity assessment of protease inhibitors against sars-cov-2. International Journal of Molecular Sciences, 22(4), 1–17. https://doi.org/10.3390/ijms22042065

Papaj, Katarzyna, Spychalska, Patrycja, Hopko, Katarzyna, Kapica, Patryk, Fisher, Andre, Lill, Markus A., Bagrowska, Weronika, Nowak, Jakub, Szleper, Katarzyna, Smieško, Martin, Kasprzycka, Anna, & Góra, Artur. (2021). Investigation of Thiocarbamates as Potential Inhibitors of the SARS-CoV-2 Mpro. Pharmaceuticals (Basel, Switzerland), 14(11), 1153. https://doi.org/10.3390/ph14111153

Mahmoud, Amr H, Masters, Matthew R, Yang, Ying, & Lill, Markus A. (2020). Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning. In Communications Chemistry (Vol. 3). Nature Research. https://doi.org/10.1038/s42004-020-0261-x

Fischer, A., Sellner, M., Neranjan, S., Lill, M. A., & Smieško, M. (2020, April 20). Potential Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 606 Million Compounds [Posted-content]. American Chemical Society (ACS). https://doi.org/10.26434/chemrxiv.11923239.v2

Fischer, André, Sellner, Manuel, Neranjan, Santhosh, Smiesko, Martin, & Lill, Markus A. (2020). Potential Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 606 Million Compounds. International Journal of Molecular Sciences, 21(10), 3626. https://doi.org/10.3390/ijms21103626

Ghanbarpour, Ahmadreza, & Lill, Markus A. (2020). Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks (Vol. 2008). arXiv. https://arxiv.org/abs/2010.15900

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

Mahmoud, Amr H., Lill, Jonas F., & Lill, Markus A. (2020). Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix (Vol. 2008). arXiv. https://arxiv.org/abs/2008.12027

Mahmoud, Amr H., Masters, Matthew R., Yang, Ying, & Lill, Markus A. (2020). Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning. Communications Chemistry, 3, 19. https://doi.org/10.1038/s42004-020-0261-x

Mahmoud, Amr, Lill, Jonas F., & Lill, Markus A. (2020). Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix. In Quantitative Biology, Biomolecules. arXiv. https://doi.org/arxiv:2008.12027

Mahmoud, A., Yang, Y., & Lill, M. (2019, January 9). Improving Atom Type Diversity and Sampling in Co-Solvent Simulations Using λ-Dynamics [Posted-content]. American Chemical Society (ACS). https://doi.org/10.26434/chemrxiv.7557224.v1

Bartolowits, Matthew D., Gast, Jonathon M., Hasler, Ashlee J., Cirrincione, Anthony M., O’Connor, Rachel J., Mahmoud, Amr H., Lill, Markus A., & Davisson, Vincent Jo. (2019). Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen. ACS Omega, 4(12), 15181–15196. https://doi.org/10.1021/acsomega.9b02079

Cassell, Robert J., Mores, Kendall L., Zerfas, Breanna L., Mahmoud, Amr H., Lill, Markus A., Trader, Darci J., & van Rijn, Richard M. (2019). Rubiscolins are naturally occurring G protein-biased delta opioid receptor peptides. European Neuropsychopharmacology : The Journal of the European College of Neuropsychopharmacology, 29(3), 450–456. https://doi.org/10.1016/j.euroneuro.2018.12.013

Kaur, Jatinder, Soto-Velasquez, Monica, Ding, Zhong, Ghanbarpour, Ahmadreza, Lill, Markus A., van Rijn, Richard M., Watts, Val J., & Flaherty, Daniel P. (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, 568–585. https://doi.org/10.1016/j.ejmech.2018.11.036

Mahmoud, Amr H., Yang, Ying, & Lill, Markus A. (2019). Improving Atom-Type Diversity and Sampling in Cosolvent Simulations Using λ-Dynamics. Journal of Chemical Theory and Computation, 15(5), 3272–3287. https://doi.org/10.1021/acs.jctc.8b00940

Mahmoud, Amr, Masters, Matthew, Yang, Ying, & Lill, Markus A. (2019). Elucidating the Multiple Roles of Hydration in Protein-Ligand Binding via Layerwise Relevance Propagation and Big Data Analytics. In Biological and Medicinal Chemistry. ChemRxiv. https://doi.org/10.26434/chemrxiv.7723223.v1

Yang, Ying, Mahmoud, Amr H., & Lill, Markus A. (2019). Modeling of Halogen-Protein Interactions in Co-Solvent Molecular Dynamics Simulations. Journal of Chemical Information and Modeling, 59(1), 38–42. https://doi.org/10.1021/acs.jcim.8b00806

Masters, M. R., Mahmoud, A. H., Yang, Y., & Lill, M. A. (2018). Efficient and Accurate Hydration Site Profiling for Enclosed Binding Sites. Journal of Chemical Information and Modeling, 58(11), 2183–2188. https://doi.org/10.1021/acs.jcim.8b00544

Cassell, R. J., Mores, K. L., Zerfas, B. L., H. Mahmoud, A., Lill, M. A., Trader, D. J., & van Rijn, R. M. (2018, October 2). Rubsicolins are naturally occurring G-protein-biased delta opioid receptor peptides [Posted-content]. Cold Spring Harbor Laboratory. https://doi.org/10.1101/433805

Wang, Y., Tang, S., Harvey, K. E., Salyer, A. E., Li, T. A., Rantz, E. K., Lill, M. A., & Hockerman, G. H. (2018). Molecular determinants of the differential modulation of Ca v 1.2 and Ca v 1.3 by nifedipine and FPL 64176. Molecular Pharmacology, 94(3), 973–983. https://doi.org/10.1124/mol.118.112441

Yang, Y., Abdallah, A. H. A., & Lill, M. A. (2018). Calculation of thermodynamic properties of bound water molecules (Vol. 1762, pp. 389–402). Humana Press Inc.humana@humanapr.com. https://doi.org/10.1007/978-1-4939-7756-7_19

Rana, N., Conley, J. M., Soto-Velasquez, M., León, F., Cutler, S. J., Watts, V. J., & Lill, M. A. (2017). Molecular Modeling Evaluation of the Enantiomers of a Novel Adenylyl Cyclase 2 Inhibitor. Journal of Chemical Information and Modeling, 57(2), 322–334. https://doi.org/10.1021/acs.jcim.6b00454

Yang, Y., Hu, B., & Lill, M. A. (2017). WATsite2.0 with PyMOL plugin: Hydration site prediction and visualization (Vol. 1611, pp. 123–134). Humana Press Inc.humana@humanapr.com. https://doi.org/10.1007/978-1-4939-7015-5_10

Lill Y, Jordan LD, Smallwood CR, Newton SM, Lill MA, Klebba PE, & Ritchie K. (2016). Confined mobility of TonB and FepA in Escherichia coli membranes. PLoS ONE, 11(12), e0160862. https://doi.org/10.1371/journal.pone.0160862

Yang, Y., & Lill, M. A. (2016). Dissecting the Influence of Protein Flexibility on the Location and Thermodynamic Profile of Explicit Water Molecules in Protein-Ligand Binding. Journal of Chemical Theory and Computation, 12(9), 4578–4592. https://doi.org/10.1021/acs.jctc.6b00411

Tabatabaei Ghomi, H., Topp, E. M., & Lill, M. A. (2016). Fibpredictor: a computational method for rapid prediction of amyloid fibril structures. Journal of Molecular Modeling, 22(9). https://doi.org/10.1007/s00894-016-3066-1

Kingsley, L. J., Esquivel-Rodríguez, J., Yang, Y., Kihara, D., & Lill, M. A. (2016). Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations. Journal of Computational Chemistry, 37(20), 1861–1865. https://doi.org/10.1002/jcc.24412

Kingsley, L. J., & Lill, M. A. (2015). Substrate tunnels in enzymes: Structure-function relationships and computational methodology. Proteins: Structure, Function and Bioinformatics, 83(4), 599–611. https://doi.org/10.1002/prot.24772

Moorthy, B. S., Ghomi, H. T., Lill, M. A., & Topp, E. M. (2015). Structural transitions and interactions in the early stages of human glucagon amyloid fibrillation. Biophysical Journal, 108(4), 937–948. https://doi.org/10.1016/j.bpj.2015.01.004

Kingsley, L. J., Wilson, G. L., Essex, M. E., & Lill, M. A. (2015). Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates. Pharmaceutical Research, 32(3), 986–1001. https://doi.org/10.1007/s11095-014-1511-3

Thompson, J. J., Ghomi, H. T., & Lill, M. A. (2014). Application of information theory to a three-body coarse-grained representation of proteins in the PDB: Insights into the structural and evolutionary roles of residues in protein structure. Proteins: Structure, Function and Bioinformatics, 82(12), 3450–3465. https://doi.org/10.1002/prot.24698

Ghomi, H. T., Thompson, J. J., & Lill, M. A. (2014). Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction? Journal of Bioinformatics and Computational Biology, 12(5). https://doi.org/10.1142/S021972001450022X

Yang, Y., Hu, B., & Lill, M. A. (2014). Analysis of factors influencing hydration site prediction based on molecular dynamics simulations. Journal of Chemical Information and Modeling, 54(10), 2987–2995. https://doi.org/10.1021/ci500426q

Kingsley, L. J., & Lill, M. A. (2014). Including ligand-induced protein flexibility into protein tunnel prediction. Journal of Computational Chemistry, 35(24), 1748–1756. https://doi.org/10.1002/jcc.23680

Pedley, A. M., Lill, M. A., & Davisson, V. J. (2014). Flexibility of PCNA-protein interface accommodates differential binding partners. PLoS ONE, 9(7). https://doi.org/10.1371/journal.pone.0102481

Kingsley, L. J., & Lill, M. A. (2014). Ensemble generation and the influence of protein flexibility on geometric tunnel prediction in cytochrome P450 enzymes. PLoS ONE, 9(6). https://doi.org/10.1371/journal.pone.0099408

Hu, B., & Lill, M. A. (2014). WATsite: Hydration site prediction program with PyMOL interface. Journal of Computational Chemistry, 35(16), 1255–1260. https://doi.org/10.1002/jcc.23616

Hu, B., & Lill, M. A. (2014). PharmDock: A pharmacophore-based docking program. Journal of Cheminformatics, 6(1). https://doi.org/10.1186/1758-2946-6-14

Lill, M. A. (2013). Foreword. In Silico Drug Discovery and Design, 2–5. https://doi.org/10.4155/ebo.13.272

Lill, M. A. (2013). In silico drug discovery and design. Future Medicine Ltd. https://doi.org/10.4155/9781909453012

Wilson, G. L., & Lill, M. A. (2013). Integrating structure- and ligand-based approaches for computer-aided drug design (pp. 190–202). Future Medicine Ltd. https://doi.org/10.4155/ebo.13.106

Xu, M., & Lill, M. A. (2013). Induced fit docking, and the use of QM/MM methods in docking. Drug Discovery Today: Technologies, 10(3). https://doi.org/10.1016/j.ddtec.2013.02.003

Hu, B., & Lill, M. A. (2013). Exploring the potential of protein-based pharmacophore models in ligand pose prediction and ranking. Journal of Chemical Information and Modeling, 53(5), 1179–1190. https://doi.org/10.1021/ci400143r

Morrow, M. E., Kim, M.-I., Ronau, J. A., Sheedlo, M. J., White, R. R., Chaney, J., Paul, L. N., Lill, M. A., Artavanis-Tsakonas, K., & Das, C. (2013). Stabilization of an unusual salt bridge in ubiquitin by the extra C-terminal domain of the proteasome-associated deubiquitinase UCH37 as a mechanism of its exo specificity. Biochemistry, 52(20), 3564–3578. https://doi.org/10.1021/bi4003106

Juncosa, J. I., Hansen, M., Bonner, L. A., Cueva, J. P., Maglathlin, R., McCorvy, J. D., Marona-Lewicka, D., Lill, M. A., & Nichols, D. E. (2013). Extensive rigid analogue design maps the binding conformation of potent N -benzylphenethylamine 5-HT2A serotonin receptor agonist ligands. ACS Chemical Neuroscience, 4(1), 96–109. https://doi.org/10.1021/cn3000668

Lill, M. (2013). Virtual screening in drug design. Methods in Molecular Biology, 993, 1–12. https://doi.org/10.1007/978-1-62703-342-8_1

Sadeghi, Ali, Ghasemi, S. Alireza, Schaefer, Bastian, Mohr, Stephan, Lill, Markus A., & Goedecker, Stefan. (2013). Metrics for measuring distances in configuration spaces. Journal of Chemical Physics, 139(18), 184118. https://doi.org/10.1063/1.4828704

Lill Y, Kaserer WA, Newton SM, Lill M, Klebba PE, & Ritchie K. (2012). Single-molecule study of molecular mobility in the cytoplasm of Escherichia coli. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 86(2), 21907. https://doi.org/10.1103/PhysRevE.86.021907

Hu, B., & Lill, M. A. (2012). Protein pharmacophore selection using hydration-site analysis. Journal of Chemical Information and Modeling, 52(4), 1046–1060. https://doi.org/10.1021/ci200620h

Cueva, J. P., Chemel, B. R., Juncosa Jr., J. I., Lill, M. A., Watts, V. J., & Nichols, D. E. (2012). Analogues of doxanthrine reveal differences between the dopamine D 1 receptor binding properties of chromanoisoquinolines and hexahydrobenzo[a]phenanthridines. European Journal of Medicinal Chemistry, 48, 97–107. https://doi.org/10.1016/j.ejmech.2011.11.039

Xu, M., & Lill, M. A. (2012). Utilizing experimental data for reducing ensemble size in flexible-protein docking. Journal of Chemical Information and Modeling, 52(1), 187–198. https://doi.org/10.1021/ci200428t

Danielson, M. L., & Lill, M. A. (2012). Predicting flexible loop regions that interact with ligands: The challenge of accurate scoring. Proteins: Structure, Function and Bioinformatics, 80(1), 246–260. https://doi.org/10.1002/prot.23199

Kortagere, S., Lill, M., & Kerrigan, J. (2012). Role of computational methods in pharmaceutical sciences. Methods in Molecular Biology, 929, 21–48. https://doi.org/10.1007/978-1-62703-50-2_3

Wilson, G. L., & Lill, M. A. (2012). Towards a realistic representation in surface-based pseudoreceptor modeling: A PDB-wide analysis of binding pockets. Molecular Informatics, 31(3-4), 259–271. https://doi.org/10.1002/minf.201100166

Lill, M. A., & Thompson, J. J. (2011). Solvent interaction energy calculations on molecular dynamics trajectories: Increasing the efficiency using systematic frame selection. Journal of Chemical Information and Modeling, 51(10), 2680–2689. https://doi.org/10.1021/ci200191m

Danielson, M. L., Desai, P. V., Mohutsky, M. A., Wrighton, S. A., & Lill, M. A. (2011). Potentially increasing the metabolic stability of drug candidates via computational site of metabolism prediction by CYP2C9: The utility of incorporating protein flexibility via an ensemble of structures. European Journal of Medicinal Chemistry, 46(9), 3953–3963. https://doi.org/10.1016/j.ejmech.2011.05.067

Cueva, J. P., Gallardo-Godoy, A., Juncosa, J. I., Vidi, P. A., Lill, M. A., Watts, V. J., & Nichols, D. E. (2011). Probing the steric space at the floor of the D1 dopamine receptor orthosteric binding domain: 7α-, 7β-, 8α-, and 8β-methyl substituted dihydrexidine analogues. Journal of Medicinal Chemistry, 54(15), 5508–5521. https://doi.org/10.1021/jm200334c

Lill, M. A. (2011). Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. Biochemistry, 50(28), 6157–6169. https://doi.org/10.1021/bi2004558

Bonner, L. A., Laban, U., Chemel, B. R., Juncosa, J. I., Lill, M. A., Watts, V. J., & Nichols, D. E. (2011). Mapping the catechol binding site in dopamine D1 receptors: Synthesis and evaluation of two parallel series of bicyclic dopamine analogues. ChemMedChem, 6(6), 1024–1040. https://doi.org/10.1002/cmdc.201100010

Wilson, G. L., & Lill, M. A. (2011). Integrating structure-based and ligand-based approaches for computational drug design. Future Medicinal Chemistry, 3(6), 735–750. https://doi.org/10.4155/fmc.11.18

Xu, M., & Lill, M. A. (2011). Significant enhancement of docking sensitivity using implicit ligand sampling. Journal of Chemical Information and Modeling, 51(3), 693–706. https://doi.org/10.1021/ci100457t

Lill, M. A., & Danielson, M. L. (2011). Computer-aided drug design platform using PyMOL. Journal of Computer-Aided Molecular Design, 25(1), 13–19. https://doi.org/10.1007/s10822-010-9395-8

Danielson, M. L., & Lill, M. A. (2010). New computational method for prediction of interacting protein loop regions. Proteins: Structure, Function and Bioinformatics, 78(7), 1748–1759. https://doi.org/10.1002/prot.22690

Ekins, S., Kortagere, S., Iyer, M., Reschly, E. J., Lill, M. A., Redinbo, M. R., & Krasowski, M. D. (2009). Challenges predicting ligand-receptor interactions of promiscuous proteins: The nuclear receptor PXR. PLoS Computational Biology, 5(12). https://doi.org/10.1371/journal.pcbi.1000594

Spreafico, Morena, Ernst, Beat, Lill, Markus A., Smiesko, Martin, & Vedani, Angelo. (2009). Mixed-Model QSAR at the Glucocorticoid Receptor: Predicting the Binding Mode and Affinity of Psychotropic Drugs. ChemMedChem, 4(1), 100–109. https://doi.org/10.1002/cmdc.200800274

Lill, M.A. (2008). Structure-Based computational approaches to drug metabolism (pp. 573–597). World Scientific Publishing Co. https://doi.org/10.1142/9789812778789_0021

Lill, M. A. (2007). Multi-dimensional QSAR in drug discovery. Drug Discovery Today, 12(23-24), 1013–1017. https://doi.org/10.1016/j.drudis.2007.08.004

Sharifi, N., Hame, E., Lill, M. A., Risbood, P., Kane Jr., C. T., Hossain, Md. T., Jones, A., Dalton, J. T., & Farrar, W. L. (2007). A bifunctional colchicinoid that binds to the androgen receptor. Molecular Cancer Therapeutics, 6(8), 2328–2336. https://doi.org/10.1158/1535-7163.MCT-07-0163

Vedani, A., Zumstein, M., Lill, M. A., & Ernst, B. (2007). Simulating α/β selectivity at the human thyroid hormone receptor: Consensus scoring using multidimensional QSAR. ChemMedChem, 2(1), 78–87. https://doi.org/10.1002/cmdc.200600212

Lill, M. A., & Vedani, A. (2006). Computational Modeling of Receptor-Mediated Toxicity (pp. 315–351). John Wiley & Sons, Inc. https://doi.org/10.1002/9780470145890.ch12

Vedani, A., Dobler, M., & Lill, M. A. (2006). The challenge of predicting drug toxicity in silico. Basic and Clinical Pharmacology and Toxicology, 99(3), 195–208. https://doi.org/10.1111/j.1742-7843.2006.pto_471.x

Lill, M. A. (2006). Computational pharmaceutical chemistry - Novel technologies for lead optimization and the prediction of ADMET properties. Chimia, 60(1-2), 33–36. https://doi.org/10.2533/000942906777675128

Lill, M. A., Dobler, M., & Vedani, A. (2006). Prediction of small-molecule binding to cytochrome P450 3A4: Flexible docking combined with multidimensional QSAR. ChemMedChem, 1(1), 73–81. https://doi.org/10.1002/cmdc.200500024

Lill, M. A., & Vedani, A. (2006). Combining 4D pharmacophore generation and multidimensional QSAR: Modeling ligand binding to the bradykinin B2 receptor. Journal of Chemical Information and Modeling, 46(5), 2135–2145. https://doi.org/10.1021/ci6001944

Lill, Yoriko, Lill, Markus A, Fahrenkrog, Birthe, Schwarz-Herion, Kyrill, Paulillo, Sara, Aebi, Ueli, & Hecht, Bert. (2006). Single Hepatitis-B Virus Core Capsid Binding to Individual Nuclear Pore Complexes in HeLa Cells. Biophysical Journal, 91(8), 3123–3130. https://doi.org/10.1529/biophysj.106.087650

Lill, M. A., Winiger, F., Vedani, A., & Ernst, B. (2005). Impact of induced fit on ligand binding to the androgen receptor: A multidimensional QSAR study to predict endocrine-disrupting effects of environmental chemicals. Journal of Medicinal Chemistry, 48(18), 5666–5674. https://doi.org/10.1021/jm050403f

Vedani, A., Dobler, M., & Lill, M. A. (2005). In silico prediction of harmful effects triggered by drugs and chemicals. 207, 398–407. https://doi.org/10.1016/j.taap.2005.01.055

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