Prof. Dr. Markus A. Lill

Tauriello, Gerardo, Lill, Yoriko, Sgrignani, Jacopo, Zoete, Vincent, Singer, Benedikt, Vallat, Brinda, Webb, Benjamin M., Garello, Thomas, Bienert, Stefan, Feig, Michael, Papaleo, Elena, Burley, Stephen K., Sali, Andrej, Lill, Markus A., Cavalli, Andrea, Dal Peraro, Matteo, & Schwede, Torsten. (2026). ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models. Journal of Molecular Biology, 438(18). https://doi.org/10.1016/j.jmb.2026.169658

Stephanie A. Vogt, Alexander J. Lander, Karl Herbine, Ekaterina Umnyakova, Jannes Felsch, Roman Aschwanden, Sarah E. Hughes, Oliver Schwardt, Markus A. Lill, Martin Smieško, John D. Lambris, Christina Lamers, & Daniel Ricklin. (2026). New Analogs of the Compstatin Family of Clinical Complement Inhibitors with Low Picomolar Target Affinity. Journal of Medicinal Chemistry, 69. https://doi.org/10.1021/acs.jmedchem.6c00832

Weddeling, Heiner G., Sowa, Sven T., Bialas, Elena, Reese, Sven, Merten, Christian, Lill, Markus, Bechthold, Andreas, & Teufel, Robin. (2026). A Hooker Oxygenase Archetype in Polyketide Biosynthesis Challenging the Baeyer-Villiger Monooxygenase Paradigm [Journal-article]. Journal of the American Chemical Society, 148(5), 5722–5733. https://doi.org/10.1021/jacs.5c21759

Diamond, Justin S., & Lill, Markus A. (2025). Neural SHAKE: geometric constraints in neural differential equations. Journal of Cheminformatics, 17(1). https://doi.org/10.1186/s13321-025-01053-w

Masters, Matthew R., Mahmoud, Amr H., & Lill, Markus A. (2025). Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions. Nature Communications, 16(1). https://doi.org/10.1038/s41467-025-63947-5

Vogt, Stephanie, Lander, Alexander J., Umnyakova, Ekaterina, Aschwanden, Roman, Hughes, Sarah E., Schwardt, Oliver, Lill, Markus A., Smieško, Martin, Lambris, John D., Lamers, Christina, & Ricklin, Daniel. (2025). New analogs of the compstatin family of clinical complement inhibitors with low picomolar target affinity [Posted-content]. In ChemRxiv. Cambridge University Press. https://doi.org/10.26434/chemrxiv-2025-dwgmp

Hinz, Florian B., Mahmoud, Amr H., & Lill, Markus A. (2025). An analysis of the modality and flexibility of the inverse stereographic normal distribution. Statistics and Computing, 35(2). https://doi.org/10.1007/s11222-025-10563-4

Hinz, Florian B., Masters, Matthew R., Nguyen, Julia T., Mahmoud, Amr H., & Lill, Markus A. (2025). Accelerated Hydration Site Localization and Thermodynamic Profiling. Journal of Chemical Information and Modeling, 65(6), 2794–2805. https://doi.org/10.1021/acs.jcim.4c02349

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

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(3), 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

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

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

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. In Methods in Molecular Biology (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. In Methods in Molecular Biology (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. In 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. In In Silico Drug Discovery and 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

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