M. Lill

发表

Metrics for measuring distances in configuration spaces.

S. Goedecker, A. Sadeghi, S. Ghasemi, 2013, The Journal of chemical physics.

Efficient incorporation of protein flexibility and dynamics into molecular docking simulations.

M. Lill, 2011, Biochemistry.

Flexibility of PCNA-Protein Interface Accommodates Differential Binding Partners

M. Lill, F. Salsbury, Anthony M. Pedley, 2014, PloS one.

Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor.

A. Vedani, M. Lill, M. Dobler, 2005, Journal of medicinal chemistry.

Reaction rates for proton transfer over small barriers and connection to transition state theory

M. Lill, V. Helms, 2001 .

Integrating structure-based and ligand-based approaches for computational drug design.

Gregory L. Wilson, M. Lill, 2011, Future medicinal chemistry.

Potential Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 606 Million Compounds

M. Lill, M. Smieško, André Fischer, 2020, International journal of molecular sciences.

In Silico Drug Discovery and Design

Markus A. Lill, M. Lill, 2013 .

Multi-dimensional QSAR in drug discovery Revie

M. Lill, 2007 .

Prediction of Small‐Molecule Binding to Cytochrome P450 3A4: Flexible Docking Combined with Multidimensional QSAR

Markus A Lill, Max Dobler, Angelo Vedani, 2006, ChemMedChem.

Structure-based Computational Approaches to Drug Metabolism

M. Lill, 2008 .

Improving Atom-Type Diversity and Sampling in Cosolvent Simulations Using λ-Dynamics.

Amr H. Mahmoud, M. Lill, Ying Yang, 2019, Journal of chemical theory and computation.

Towards a Realistic Representation in Surface‐Based Pseudoreceptor Modeling: a PDB‐Wide Analysis of Binding Pockets

Gregory L. Wilson, M. Lill, 2012, Molecular informatics.

Ligand pathways in estrogen-related receptors

M. Lill, M. Smieško, André Fischer, 2022, Journal of biomolecular structure & dynamics.

In silico prediction of harmful effects triggered by drugs and chemicals.

A. Vedani, M. Lill, M. Dobler, 2005, Toxicology and applied pharmacology.

Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning

Ying Yang, Markus A. Lill, Amr H. Mahmoud, 2020, Communications Chemistry.

Instantaneous generation of protein hydration properties from static structures

Amr H. Mahmoud, M. Lill, A. Ghanbarpour, 2020, Communications Chemistry.

WATsite2.0 with PyMOL Plugin: Hydration Site Prediction and Visualization.

Markus A Lill, Ying Yang, M. Lill, 2017, Methods in molecular biology.

Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks.

Markus A. Lill, Ahmadreza Ghanbarpour, M. Lill, 2020, 2010.15900.

Extensive rigid analogue design maps the binding conformation of potent N-benzylphenethylamine 5-HT2A serotonin receptor agonist ligands.

D. E. Nichols, M. Lill, J. McCorvy, 2013, ACS chemical neuroscience.

Optimization of a 1,3,4-oxadiazole series for inhibition of Ca2+/calmodulin-stimulated activity of adenylyl cyclases 1 and 8 for the treatment of chronic pain.

J. Kaur, M. Lill, V. Watts, 2019, European journal of medicinal chemistry.

Analysis of Factors In fl uencing Hydration Site Prediction Based on Molecular Dynamics Simulations

M. Lill, Bingjie Hu, Ying Yang, 2014 .

Potential Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 606 Million Compounds

M. Lill, M. Smieško, André Fischer, 2020, International journal of molecular sciences.

Dynamic water networks in cytochrome C oxidase from Paracoccus denitrificans investigated by molecular dynamics simulations.

H. Michel, M. Lill, V. Helms, 2004, Biophysical journal.

D EEP L EARNING M ODEL FOR F LEXIBLE AND E FFI CIENT P ROTEIN -L IGAND D OCKING

Amr H. Mahmoud, M. Lill, Matthew Masters, 2022 .

Potential Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 606 Million Compounds

M. Lill, M. Smieško, André Fischer, 2020, International journal of molecular sciences.

Kinetics of the initial steps of G protein-coupled receptor-mediated cellular signaling revealed by single-molecule imaging.

B. Hecht, M. Lill, K. L. Martinez, 2005, Chemphyschem : a European journal of chemical physics and physical chemistry.

Computational Selectivity Assessment of Protease Inhibitors against SARS-CoV-2

M. Lill, Karolina Mitusińska, Maria Bzówka, 2021, International journal of molecular sciences.

SIE calculations on molecular dynamics trajectories: Increasing the efficiency using systematic frame selection

M. Lill, Jared J. Thompson, 2011 .

Elucidating the Multiple Roles of Hydration in Protein-Ligand Binding via Layerwise Relevance Propagation and Big Data Analytics

Amr H. Mahmoud, M. Lill, Ying Yang, 2019 .

Molecular Determinants of the Differential Modulation of Cav1.2 and Cav1.3 by Nifedipine and FPL 64176

M. Lill, Yuchen Wang, T. Li, 2018, Molecular Pharmacology.

Probing the steric space at the floor of the D1 dopamine receptor orthosteric binding domain: 7α-, 7β-, 8α-, and 8β-methyl substituted dihydrexidine analogues.

M. Lill, Pierre-Alexandre Vidi, V. Watts, 2011, Journal of medicinal chemistry.

Long-chain carboxychromanols, metabolites of vitamin E, are potent inhibitors of cyclooxygenases

Matthew L. Danielson, Xinmin Yin, M. Lill, 2008, Proceedings of the National Academy of Sciences.

Calculation of Thermodynamic Properties of Bound Water Molecules.

M. Lill, Ying Yang, Amr H. A. Abdallah, 2018, Methods in molecular biology.

Chapter 10 WATsite 2 . 0 with PyMOL Plugin : Hydration Site Prediction and Visualization

M. Lill, Bingjie Hu, Ying Yang, 2017 .

Rubiscolins are naturally occurring G protein-biased delta opioid receptor peptides

Amr H. Mahmoud, M. Lill, Breanna L. Zerfas, 2019, European Neuropsychopharmacology.

Rubsicolins are naturally occurring G-protein-biased delta opioid receptor peptides

M. Lill, Breanna L. Zerfas, R. V. van Rijn, 2018, bioRxiv.

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

M. Lill, Jared J. Thompson, Hamed Tabatabaei Ghomi, 2014, Proteins.

H IGH -C ONTENT S IMILARITY -B ASED V IRTUAL S CREENING U SING A D ISTANCE -A WARE T RANS FORMER M ODEL

Amr H. Mahmoud, M. Lill, M. Sellner, 2022 .

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results.

M. Lill, M. Smieško, André Fischer, 2021, Journal of medicinal chemistry.

Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 687 Million Compounds

Markus A. Lill, Martin Smieško, André Fischer, 2020 .

Mapping the Catechol Binding Site in Dopamine D1 Receptors: Synthesis and Evaluation of Two Parallel Series of Bicyclic Dopamine Analogues

M. Lill, V. Watts, J. Juncosa, 2011, ChemMedChem.

Analogues of doxanthrine reveal differences between the dopamine D1 receptor binding properties of chromanoisoquinolines and hexahydrobenzo[a]phenanthridines.

M. Lill, V. Watts, J. Cueva, 2012, European journal of medicinal chemistry.

Role of computational methods in pharmaceutical sciences.

Sandhya Kortagere, Markus Lill, John Kerrigan, 2012, Methods in molecular biology.

Substrate tunnels in enzymes: Structure–function relationships and computational methodology

Markus A Lill, Laura J Kingsley, Laura J. Kingsley, 2015, Proteins.

Computer-aided drug design platform using PyMOL

Matthew L. Danielson, Markus A. Lill, M. Lill, 2011, J. Comput. Aided Mol. Des..

Evaluation of Xa inhibitors as potential inhibitors of the SARS-CoV-2 Mpro protease

M. Lill, Maria Bzówka, Artur Góra, 2022, PloS one.

Fibpredictor: a computational method for rapid prediction of amyloid fibril structures

M. Lill, E. Topp, Hamed Tabatabaei Ghomi, 2016, Journal of Molecular Modeling.

A bifunctional colchicinoid that binds to the androgen receptor

W. Farrar, M. Lill, J. Dalton, 2007, Molecular Cancer Therapeutics.

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

Sean Ekins, Sandhya Kortagere, Matthew D. Krasowski, 2009, PLoS Comput. Biol..

Ranking protein–protein docking results using steered molecular dynamics and potential of mean force calculations

Daisuke Kihara, Ying Yang, Juan Esquivel-Rodríguez, 2016, J. Comput. Chem..

PharmDock: a pharmacophore-based docking program

Markus A. Lill, Bingjie Hu, M. Lill, 2014, Journal of Cheminformatics.

Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen

Amr H. Mahmoud, M. Lill, V. Davisson, 2019, ACS omega.

Mixed‐Model QSAR at the Glucocorticoid Receptor: Predicting the Binding Mode and Affinity of Psychotropic Drugs

A. Vedani, M. Lill, B. Ernst, 2009, ChemMedChem.

Predicting flexible loop regions that interact with ligands: The challenge of accurate scoring

Markus A Lill, Matthew L. Danielson, Matthew L Danielson, 2012, Proteins.

Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix.

Markus A. Lill, Amr H. Mahmoud, Jonas F. Lill, 2020, 2008.12027.

Investigation of Thiocarbamates as Potential Inhibitors of the SARS-CoV-2 Mpro

M. Lill, Weronika Bagrowska, Artur Góra, 2021, Pharmaceuticals.

On-the-fly Prediction of Protein Hydration Densities and Free Energies using Deep Learning pdf

Markus A. Lill, Amr H. Mahmoud, Ahmadreza Ghanbarpour, 2020, ArXiv.

Virtual screening in drug design.

Markus Lill, M. Lill, 2013, Methods in molecular biology.

Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation

Amr H. Mahmoud, M. Lill, Matthew Masters, 2022, J. Chem. Inf. Model..

Ensemble Generation and the Influence of Protein Flexibility on Geometric Tunnel Prediction in Cytochrome P450 Enzymes

Markus A. Lill, Laura J. Kingsley, M. Lill, 2014, PloS one.

Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbation

Amr H. Mahmoud, M. Lill, Soo-Jung Lee, 2023, ArXiv.

Structural transitions and interactions in the early stages of human glucagon amyloid fibrillation.

M. Lill, E. Topp, B. Moorthy, 2015, Biophysical journal.

The challenge of predicting drug toxicity in silico.

Markus A Lill, Max Dobler, Angelo Vedani, 2006, Basic & clinical pharmacology & toxicology.

Proton shuttle in green fluorescent protein studied by dynamic simulations

M. Lill, V. Helms, 2002, Proceedings of the National Academy of Sciences of the United States of America.

New computational method for prediction of interacting protein loop regions

Markus A Lill, Matthew L. Danielson, Matthew L Danielson, 2010, Proteins.

Multi-dimensional QSAR in drug discovery.

Markus A Lill, M. Lill, 2007, Drug discovery today.

Simulating α/β Selectivity at the Human Thyroid Hormone Receptor: Consensus Scoring Using Multidimensional QSAR

Markus A. Lill, Beat Ernst, Angelo Vedani, 2007 .

Combining 4D Pharmacophore Generation and Multidimensional QSAR: Modeling Ligand Binding to the Bradykinin B2 Receptor

Markus A. Lill, Angelo Vedani, A. Vedani, 2006, J. Chem. Inf. Model..

Virtual test kits for predicting harmful effects triggered by drugs and chemicals mediated by specific proteins.

A. Vedani, M. Lill, M. Dobler, 2005, ALTEX.

Raptor: combining dual-shell representation, induced-fit simulation, and hydrophobicity scoring in receptor modeling: application toward the simulation of structurally diverse ligand sets.

Markus A Lill, Max Dobler, Angelo Vedani, 2004, Journal of medicinal chemistry.

Exploring the Potential of Protein-Based Pharmacophore Models in Ligand Pose Prediction and Ranking

Markus A. Lill, Bingjie Hu, M. Lill, 2013, J. Chem. Inf. Model..

Confined Mobility of TonB and FepA in Escherichia coli Membranes

Lorne D. Jordan, K. Ritchie, M. Lill, 2016, PloS one.

Single-molecule study of molecular mobility in the cytoplasm of Escherichia coli.

K. Ritchie, M. Lill, P. Klebba, 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

Solvent Interaction Energy Calculations on Molecular Dynamics Trajectories: Increasing the Efficiency Using Systematic Frame Selection

Markus A. Lill, Jared J. Thompson, M. Lill, 2011, J. Chem. Inf. Model..

Single hepatitis-B virus core capsid binding to individual nuclear pore complexes in Hela cells.

B. Hecht, U. Aebi, Kyrill Schwarz-Herion, 2006, Biophysical journal.

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.

M. Lill, M. E. Morrow, C. Das, 2013, Biochemistry.

Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks.

M. Lill, A. Ghanbarpour, 2020, 2010.15900.

A Medicinal Chemist's Guide to Molecular Interactions

Markus A. Lill, M. Lill, 2013 .

Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning

Amr H. Mahmoud, M. Lill, Ying Yang, 2020, Communications Chemistry.

Compact parameter set for fast estimation of proton transfer rates

Volkhard Helms, Markus A. Lill, M. Lill, 2001 .

Molecular dynamics simulation of proton transport with quantum mechanically derived proton hopping rates (Q-HOP MD)

Volkhard Helms, Markus A. Lill, M. Lill, 2001 .

Protein Pharmacophore Selection Using Hydration-Site Analysis

Markus A. Lill, Bingjie Hu, M. Lill, 2012, J. Chem. Inf. Model..

Induced fit docking, and the use of QM/MM methods in docking.

Markus A Lill, Mengang Xu, M. Lill, 2013, Drug discovery today. Technologies.

Including ligand‐induced protein flexibility into protein tunnel prediction

Markus A. Lill, Laura J. Kingsley, M. Lill, 2014, J. Comput. Chem..

Modeling of Halogen-Protein Interactions in Co-Solvent Molecular Dynamics Simulations

Ying Yang, Markus A Lill, Amr H. Mahmoud, 2018, J. Chem. Inf. Model..

Molecular Modeling Evaluation of the Enantiomers of a Novel Adenylyl Cyclase 2 Inhibitor

Neha Rana, Markus A. Lill, Stephen J. Cutler, 2017, J. Chem. Inf. Model..

Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes

Justin S. Diamond, M. Lill, 2023, ArXiv.

Integrating structure-and ligand-based approaches for computer-aided drug design

Markus A. Lill, Gregory L. Wilson, M. Lill, 2013 .

Improving Protein-peptide Interface Predictions in the Low Data Regime

Justin S. Diamond, M. Lill, 2023, ArXiv.

In silico prediction of receptor-mediated environmental toxic phenomena—application to endocrine disruption

A. Vedani, M. Lill, M. Dobler, 2005, SAR and QSAR in environmental research.

Utilizing Experimental Data for Reducing Ensemble Size in Flexible-Protein Docking

Mengang Xu, Markus A. Lill, M. Lill, 2012, J. Chem. Inf. Model..

Significant Enhancement of Docking Sensitivity Using Implicit Ligand Sampling

Mengang Xu, Markus A. Lill, M. Lill, 2011, J. Chem. Inf. Model..

Novel ligands for the chemokine receptor-3 (CCR3): a receptor-modeling study based on 5D-QSAR.

Markus A Lill, Max Dobler, Angelo Vedani, 2005, Journal of medicinal chemistry.

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.

Markus A Lill, Matthew L. Danielson, Matthew L Danielson, 2011, European journal of medicinal chemistry.

Improving Atom-Type Diversity and Sampling in Cosolvent Simulations Using λ-Dynamics.

Amr H. Mahmoud, M. Lill, Ying Yang, 2019, Journal of chemical theory and computation.

Rubsicolins are naturally occurring G-protein-biased delta opioid receptor peptides

G. Pasternak, M. Lill, Ying-Xian Pan, 2018, bioRxiv.

Cover Picture: Simulating α/β Selectivity at the Human Thyroid Hormone Receptor: Consensus Scoring Using Multidimensional QSAR (ChemMedChem 1/2007)

A. Vedani, M. Lill, B. Ernst, 2007 .

Efficient and Accurate Hydration Site Profiling for Enclosed Binding Sites

Ying Yang, Markus A Lill, Amr H. Mahmoud, 2018, J. Chem. Inf. Model..

Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction?

Markus A. Lill, Jared J. Thompson, Hamed Tabatabaei Ghomi, 2014, J. Bioinform. Comput. Biol..

Substrate tunnels in enzymes: Structure–function relationships and computational methodology

Laura J. Kingsley, M. Lill, 2015, Proteins.

WATsite: Hydration site prediction program with PyMOL interface

Markus A. Lill, Bingjie Hu, M. Lill, 2014, J. Comput. Chem..

Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility

Amr H. Mahmoud, M. Lill, Matthew Masters, 2023, J. Chem. Inf. Model..

Efficient virtual high-content screening using a distance-aware transformer model

Amr H. Mahmoud, M. Lill, M. Sellner, 2023, Journal of Cheminformatics.

Computational Modeling of Receptor‐Mediated Toxicity

A. Vedani, M. Lill, 2006 .

Mixed‐Model QSAR at the Glucocorticoid Receptor: Predicting the Binding Mode and Affinity of Psychotropic Drugs

A. Vedani, M. Lill, B. Ernst, 2009, ChemMedChem.

Predicting the toxic potential of drugs and chemicals in silico.

Markus A Lill, Max Dobler, Angelo Vedani, 2007, ALTEX.

Combining Structure- and Ligand-Based Approaches to Improve Site of Metabolism Prediction in CYP2C9 Substrates

Markus A. Lill, Gregory L. Wilson, Laura J. Kingsley, 2014, Pharmaceutical Research.