Yang, Y.; Lill, M.A. Dissecting the Influence of Protein Flexibility on the Location and Thermodynamic Profile of Explicit Water Molecules in Protein-Ligand Binding. J. Chem. Theory Comput. in press.

Ghomi, H.T.; Topp, E.M.; Lill, M.A. Fibpredictor: A computational method for rapid prediction of amyloid fibril structures. J. Mol. Model. 22, 2016, 206.

Lill, Y.; Jordan, L.D.; Smallwood, C.R.; Newton, S.M.; Lill, M.A.; Klebba, P.E.; Ritchie, K. Confined mobility of TonB and FepA in Escherichia coli membranes. PLoS One in press.

Kingsley, L.J.; Esquivel-Rodriguez, J.; Yang, Y.; Kihara, D.; Lill M.A. Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations. J. Comput. Chem. 37, 2016, 1861-5.


Moorthy, B.S.; Ghomi, H.T.; Lill, M.A.; Topp, E.M. Structural transitions and interactions in the early stages of human glucagon amyloid fibrillation. Biophys. J. 108, 2015, 937-948.

Kingsley, L.J.; Lill, M.A. Substrate tunnels in enzymes: Structure-function relationships and computational methodology. Proteins 83, 2015, 599-611.

Kingsley, L.J.; Wilson, G.L.; Essex, M.E.; Lill, M.A. Combining Structure- and Ligand-Based Approaches to Improve Site of Metabolism Prediction in CYP2C9 Substrates. Pharm. Res. 32, 2015, 986-1001.


Thompson, J.J.; Ghomi, H.T.; Lill, M.A. 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 82, 2014, 3450-3465.

Yang, Y.; Hu, B.; Lill, M.A. Analysis of factors influencing hydration site prediction based on molecular dynamics simulations. J. Chem. Inf. Model. 54, 2014, 2987-2995.

Ghomi, H.T.; Thompson, J.J.; Lill, M.A. Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction? J. Bioinform. Comput. Biol., 2014, 1450022.

Kingsley, L.J.; Lill, M.A. Including ligand-induced protein flexibility into protein tunnel prediction. J. Comput. Chem. 35, 2014, 1748-1756.

Pedley, A.M.; Lill, M.A.; Davisson V.J. Flexibility of PCNA-Protein Interface Accommodates Differential Binding Partners. PLoS One 9, 2014, e102481.

Kingsley, L.J.; Lill, M.A. Ensemble generation and the influence of protein flexibility on geometric tunnel prediction in cytochrome p450 enzymes. PLoS One 9, 2014, e99408.

Hu, B.; Lill, M.A. WATsite: Hydration site prediction program with PyMOL interface. J.Comput. Chem., 35, 2014, 1255-60.

Hu, B.; Lill, M.A. PharmDock: a pharmacophore-based docking program. J. Cheminform., 6, 2014, 14.


Sadeghi, A.; Ghasemi, S.A.; Schaefer, B.; Mohr, S.; Lill M.A.; Goedecker, S. Metrics for measuring distances in configuration spaces. J. Chem. Phys. 139, 2013, 184118.

Lill, M.A. In silico drug discovery and design. In In silico drug discovery and design (Eds. M.A. Lill), Future Science, 2013, 3-5.

Wilson, G.L.; Lill, M.A. Integrating structure- and ligand-based approaches for computer-aided drug design. In In silico drug discovery and design (Eds. M.A. Lill), Future Science, 2013, 191-202.

Xu, M.; Lill, M.A. Induced fit docking, and the use of QM/MM methods in docking. Drug Discov. Today Technol. 10, 2013, e411-e418.

Hu, B.; Lill, M.A. Exploring the potential of protein-based pharmacophore models in ligand pose prediction and ranking. J. Chem. Inf. Model., 53,2013, 1179-1190.

Morrow, M.; Kim, M.-I.; Ronau, J.; Sheedlo, M.; White, R.; Chaney, J.; Paul, L.; Lill, M.; Artavanis-Tsakonas, K.; Das, C. 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, 2013, 3564-3578.

Lill, M.A. Virtual Screening in Drug Design. In Methods in Molecular Biology: In Silico Models for Drug Discovery (Eds. S. Kortagere), Springer, 993, 2013, 1-13.

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. Extensive rigid analogue design maps the binding conformation of potent N-benzylphenethylamine 5-HT 2A serotonin receptor agonist ligands. ACS Chem. Neuro., 4, 2013, 96-109.


Hu, B.; Lill, M.A. Protein pharmacophore selection using hydration-site analysis. J. Chem. Inf. Model., 52, 2012, 1046-1060.

Wilson, G.L.; Lill, M.A. Towards a realistic representation in surface-based pseudoreceptor modelling: a PDB-wide analysis of binding pockets. Mol. Inf., 31, 2012, 259-271.

Lill, Y.; Kaserer, W.A.; Newton, S.M.; Lill, M.A.; Klebba, P.E.; Ritchie, K.P. A single molecule study of molecular mobility in the cytoplasm of E. coli. Phys. Rev. E., 86, 2012, 021907.

Cueva, J.P.; Chemel, B.R.; Juncosa, J.I.; Lill, M.A.; Watts, V.J.; Nichols, D.E. Analogues of doxanthrine reveal differences between the dopamine D1 receptor binding properties of chromanoisoquinolines and hexahydrobenzo[a]phenanthridines. Eur. J. Med. Chem., 48, 2012, 97-107.

Kortagere, S.; Lill, M.A.; Kerrigan, J. Role of Computational methods in Pharmaceutical Sciences. In Methods in Molecular Biology: Computational Toxicology (Eds. S. Kortagere), Springer, 929, 2012, 21-48.

Danielson, M.L.; Lill, M.A. Predicting flexible loop regions that interact with ligands: The challenge of accurate scoring. Proteins, 80, 2012, 246-260.

Xu, M.; Lill, M.A. Utilizing experimental data for reducing ensemble size in flexible-protein docking. J. Chem. Inf. Model., 52, 2012, 187-198.


Lill, M.A.; Thompson, J.J. SIE calculations on molecular dynamics trajectories: Increasing the efficiency using systematic frame selection. J. Chem. Inf. Model., 51, 2011, 2680-2689.

Cueva, J.P.; Gallardo-Godoy, A.; Juncosa, J.I.; Vidi, P.A.; Lill, M.A.; Watts, V.J.; Nichols, D.E. Probing the Steric Space at the Floor of the D(1) Dopamine Receptor Orthosteric Binding Domain: 7α-, 7β-, 8α-, and 8β-Methyl Substituted Dihydrexidine Analogues. J. Med. Chem., 54, 2011, 5508-5521.

Lill, M.A. Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. Biochemistry, 50, 2011, 6157-6169.

Danielson, M.L.; Desai, P.V.; Mohutsky, M.A.; Wrighton, S.A.; Lill, M.A. 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. Eur. J. Med. Chem., 46, 2011, 3953-3963.

Bonner, L.A.; Laban, U.; Chemel, B.R.; Juncosa, J.I.; Lill, M.A.; Watts, V.J,; Nichols, D.E. Mapping the Catechol Binding Site in Dopamine D1 Receptors: Synthesis and Evaluation of Two Parallel Series of Bicyclic Dopamine Analogues. ChemMedChem, 6, 2011, 1024-1040.

Wilson, G.L.; Lill, M.A. Integrating structure-based and ligand-based approaches for computational drug design. Future Med. Chem., 3, 2011, 735-750.

Xu, M.; Lill, M.A. Significant enhancement of docking sensitivity using implicit ligand sampling. J. Chem. Inf. Model., 51, 2011, 693-706.

Lill, M.A.; Danielson, M.L. Computer-aided drug design platform using PyMOL. J. Comput. Aided Mol. Des., 25, 2011, 13-19.


Danielson, M.L.; Lill, M.A. New computational method for prediction of interacting protein-loop regions. Proteins 78, 2010, 1748-1759.


Ekins, S.; Kortagere, S.; Iyer, M.; Reschly, E.J.; Lill, M.A.; Redinbo, M.R.; Krasowski, M.D. Challenges predicting ligand-receptor interactions of promiscuous proteins: The nuclear receptor PXR. PLoS Comput. Biol. 5, 2009, e1000594.

Spreafico, M.; Ernst, B.; Lill, M.A.; Smiesko, M.; Vedani, A. Mixed-model QSAR at the glucocorticoid receptor: predicting the binding mode and affinity of psychotropic drugs. ChemMedChem, 4, 2009, 100-109.


Jiang, Q.; Yin, X.; Lill, M.A.; Danielson, M.L.; Freiser, H.; Huang, J. Long-chain carboxychromanols, metabolites of vitamin E, are potent inhibitors of cyclooxygenases. Proc. Natl. Acad. Sci., 105, 2008, 20464-20469.

Lill, M.A. Structure-based computational approaches to drug metabolism. In Computational Structural Biology: Methods and Applications (Eds. T. Schwede; M.C. Peitsch), World Scientific Publishing Company, 2008, 573-597.


Lill, M.A. Multi-dimensional QSAR in drug discovery. Drug Discov. Today 12, 2007 1013-1017.

Sharifi, N.; Hamel, E.; Lill, M.A.; Risbood, P.; Kane, C.T. Jr.; Hossain, M.T.; Dalton, J.; Farrar, W.L. A bifunctional colchicinoid that binds the androgen receptor. Mol. Cancer Ther. 6, 2007, 2328-2336.

Lill, M.A.; Vedani, A. Computational modeling of receptor-mediated toxicity. In Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals (Eds. S. Ekins), John Wiley & Sons, Inc., 2007, 315-351.

Vedani, A.; Lill, M.A.; Dobler, M. Predicting the toxic potential of drugs and chemicals in silico. ALTEX 24, 2007, 63-66.

Vedani, A; Zumstein, M.; Lill, M.A.; Ernst, B. Simulating α/β selectivity at the human thyroid hormone receptor: Consensus scoring using multi-dimensional QSAR. ChemMedChem 2, 2007, 78-87.


Lill, M.A.; Vedani, A. Combining 4D pharmacophore generation and multidimensional QSAR: Modeling ligand binding to the Bradykinin B2 receptor. J. Chem. Inf. Model. 46, 2006, 2135-2145.

Lill, Y.; Lill, M.A.; Fahrenkrog, B.; Sschwarz-Herion, K.; Paulillo, S.; Aebi, U.; Hecht, B. Single Hepatitis-B virus core capsid binding to individual nuclear pore complexes in HeLa cells. Biophys. J. 91, 2006, 3123-3130.

Lill, M.A. Computational Pharmaceutical Chemistry: Novel Technologies for Lead Optimization and the Prediction of ADMET Properties. Chimia 60, 2006, 33-36.

Vedani, A.; Dobler, M.; Lill, M.A. The challenge of predicting drug toxicity in silico. Basic Clin. Pharmacol. Toxic. 99, 2006, 195-208.

Lill, M.A.; Dobler, M.; Vedani, A. In Silico Prediction of Small-Molecule Binding to Cytochrome P450 3A4: Flexible Docking Combined with Multidimensional QSAR. ChemMedChem 1, 2006, 73-81.


Lill, M.A.; Winiger, F.; Vedani, A.; Ernst, B. The role of induced fit for quantifying ligand binding to the androgen receptor. J. Med. Chem. 48, 2005, 5666-5674.

Vedani, A.; Dobler, M; Lill, M.A. Virtual test kits for predicting harmful effects triggered by drugs and chemicals mediated by specific proteins. ALTEX 22, 2005, 123-134.

Lill, Y.; Martinez, K. L.; Lill, M.A.; Meyer, B.H.; Vogel, H.; Hecht, B. Kinetics of the initial steps of G-protein coupled receptor mediated cellular signaling revealed by single molecule imaging. ChemPhysChem 6, 2005, 1633-1640.

Lill, M.A.; Dobler, M.; Vedani, A. Multi-dimensional QSAR in drug design: Application to GPCRs and nuclear receptors. Curr. Comp. Aid. Drug Des. 1, 2005, 307-324.

Vedani, A.; Dobler, M.; Lill, M.A. Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor. J. Med. Chem. 48, 2005, 3700-3703.

Lill, M.A.; Dobler, M.; Vedani, A. In silico prediction of receptor-mediated environmental toxic phenomena ― Application towards endocrine disruption. SAR QSAR Environ. Res. 16, 2005, 149-169.

Vedani, A.; Dobler, M.; Lill, M.A. In silico prediction of harmful effects triggered by drugs and chemicals. Toxicol. Appl. Pharm. 207, 2005, S398-S407.

Vedani, A.; Dobler M.; Dollinger H.; Hasselbach, K.-M.; Birke F; Lill, M.A. Novel ligands for the chemokine receptor-3 (CCR3): A receptor-modeling study based on 5D-QSAR. J. Med. Chem. 48, 2005, 1515-1527.


Lill, M.A.; Vedani, A.; Dobler, M. Raptor - combining dual-shell representation, induced-fit simulation and hydrophobicity scoring in receptor modeling: Application towards the simulation of structurally diverse ligand sets. J. Med. Chem. 47, 2004, 6174-6186.

Olkhova, E.; Hutter, M.; Lill, M.A.; Helms, V.; Michel, H. Possible water networks in cytochrome c oxidase from paracoccus denitrificans investigated by molecular dynamics simulations. Biophys. J. 86, 2004, 1873-1889.


Dobler, M.; Lill, M.A.; Vedani, A. From crystal structures and their analysis to the in silico prediction of toxic phenomena. Helv. Chim. Acta 86, 2003, 1554-1568.

Vedani, A.; Dobler, M.; Lill, M.A. Internet laboratory for predicting harmful effects triggered by drugs and chemicals - A progress report. ALTEX 20, 2003, 85-91.

Lill, M.A. Predictive toxicology: in silico modelling and expert systems. ALTEX 20, 2003, 104-105.


Lill, M.A.; Helms, V. Proton shuttle in green fluorescent protein studied by dynamic simulations. Proc. Natl. Acad. Sci. 99, 2002, 2778-2781.

Helms, V.; Lill., M.A.; Gorba, C. Developments in Mathematical and Experimental Physics, Kluwer Academic Press, Computer Simulation Meets Molecular Biology (2002).


Lill, M.A.; Helms, V. Molecular dynamics simulation of proton transport with quantum mechanically derived proton hopping rates (Q-HOP MD). J. Chem. Phys. 115, 2001, 7993-8005.

Lill, M.A.; Helms, V. Reaction rates for proton transfer over small barriers and connection to transition state theory. J. Chem. Phys. 115, 2001, 7985-7992.

Lill, M.A.; Helms, V. Compact parameter set for fast estimation of proton transfer rates. J. Chem. Phys. 114, 2001, 1125-1132.


Lill, M.A.; Hutter, M. C.; Helms, V. Accounting for environmental effects in ab initio calculations of proton transfer barriers. J. Phys. Chem.104, 2000, 8283-8289.

Marten, J.; Pschiwul, T.; Lill, M.A.; Reinhard, P.-G.; Toepffer, C.; Zwicknagel, G. Molecular dynamics simulations of microfields in strongly correlated plasmas. J. Phys. IV France 10, 2000, 497-500.