Combining 4D Pharmacophore Generation and Multidimensional QSAR: Modeling Ligand Binding to the Bradykinin B2 Receptor. - Markus A. Lill; Angelo Vedani (J. Chem. Inf. Model., 2006)

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR. - Sean Ekins; Sandhya Kortagere; Manisha Iyer; Erica J. Reschly; Markus A. Lill; Matthew R. Redinbo; Matthew D. Krasowski (PLoS Comput. Biol., 2009)

Computer-aided drug design platform using PyMOL. - Markus A. Lill; Matthew L. Danielson (J. Comput. Aided Mol. Des., 2011)

Solvent Interaction Energy Calculations on Molecular Dynamics Trajectories: Increasing the Efficiency Using Systematic Frame Selection. - Markus A. Lill; Jared J. Thompson (J. Chem. Inf. Model., 2011)

Significant Enhancement of Docking Sensitivity Using Implicit Ligand Sampling. - Mengang Xu; Markus A. Lill (J. Chem. Inf. Model., 2011)

Protein Pharmacophore Selection Using Hydration-Site Analysis. - Bingjie Hu; Markus A. Lill (J. Chem. Inf. Model., 2012)

Utilizing Experimental Data for Reducing Ensemble Size in Flexible-Protein Docking. - Mengang Xu; Markus A. Lill (J. Chem. Inf. Model., 2012)

Exploring the Potential of Protein-Based Pharmacophore Models in Ligand Pose Prediction and Ranking. - Bingjie Hu; Markus A. Lill (J. Chem. Inf. Model., 2013)

Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction? - Hamed Tabatabaei Ghomi; Jared J. Thompson; Markus A. Lill (J. Bioinform. Comput. Biol., 2014)

WATsite: Hydration site prediction program with PyMOL interface. - Bingjie Hu; Markus A. Lill (J. Comput. Chem., 2014)

Including ligand-induced protein flexibility into protein tunnel prediction. - Laura J. Kingsley; Markus A. Lill (J. Comput. Chem., 2014)

PharmDock: a pharmacophore-based docking program. - Bingjie Hu; Markus A. Lill (J. Cheminformatics, 2014)

IterTunnel; a method for predicting and evaluating ligand EgressTunnels in proteins with buried active sites. - Laura J. Kingsley; Markus A. Lill (J. Cheminformatics, 2014)

Analysis of Factors Influencing Hydration Site Prediction Based on Molecular Dynamics Simulations. - Ying Yang; Bingjie Hu; Markus A. Lill (J. Chem. Inf. Model., 2014)

Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations. - Laura J. Kingsley; Juan Esquivel-Rodríguez; Ying Yang; Daisuke Kihara; Markus A. Lill (J. Comput. Chem., 2016)

Molecular Modeling Evaluation of the Enantiomers of a Novel Adenylyl Cyclase 2 Inhibitor. - Neha Rana; Jason M. Conley; Monica Soto-Velasquez; Francisco León; Stephen J. Cutler; Val J. Watts; Markus A. Lill (J. Chem. Inf. Model., 2017)

Efficient and Accurate Hydration Site Profiling for Enclosed Binding Sites. - Matthew R. Masters; Amr H. Mahmoud; Ying Yang; Markus A. Lill (J. Chem. Inf. Model., 2018)

Modeling of Halogen-Protein Interactions in Co-Solvent Molecular Dynamics Simulations. - Ying Yang; Amr H. Mahmoud; Markus A. Lill (J. Chem. Inf. Model., 2019)

On-the-fly Prediction of Protein Hydration Densities and Free Energies using Deep Learning. - Ahmadreza Ghanbarpour; Amr H. Mahmoud; Markus A. Lill (CoRR, 2020)

Conformational Changes of Thyroid Receptors in Response to Antagonists. - André Fischer; Gabriela Frehner; Markus A. Lill; Martin Smiesko (J. Chem. Inf. Model., 2021)

Computational Assessment of Combination Therapy of Androgen Receptor-Targeting Compounds. - André Fischer; Florian Häuptli; Markus A. Lill; Martin Smiesko (J. Chem. Inf. Model., 2021)

Influence of Assimilation Effects on Recommender Systems. - Markus A. Lill; Martin Spann (ICIS, 2022)

Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation. - Amr H. Mahmoud; Matthew Masters; Soo Jung Lee; Markus A. Lill (J. Chem. Inf. Model., 2022)

Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbation. - Soo Jung Lee; Amr H. Mahmoud; Markus A. Lill (CoRR, 2023)

Improving Protein-peptide Interface Predictions in the Low Data Regime. - Justin S. Diamond; Markus A. Lill (CoRR, 2023)

Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes. - Justin S. Diamond; Markus A. Lill (CoRR, 2023)

Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility. - Matthew R. Masters; Amr H. Mahmoud; Yao Wei; Markus A. Lill (J. Chem. Inf. Model., 2023)

Prediction of molecular field points using SE(3)-transformer model. - Florian B. Hinz; Amr H. Mahmoud; Markus A. Lill (Mach. Learn. Sci. Technol., 2023)

Efficient virtual high-content screening using a distance-aware transformer model. - Manuel S. Sellner; Amr H. Mahmoud; Markus A. Lill (J. Cheminformatics, 2023)

Neural SHAKE: Geometric Constraints in Graph Generative Models. - Justin S. Diamond; Markus A. Lill (ICANN (10), 2024)

Accelerated Hydration Site Localization and Thermodynamic Profiling. - Florian B. Hinz; Matthew R. Masters; Julia N. Kieu; Amr H. Mahmoud; Markus A. Lill (CoRR, 2024)

Accelerated Hydration Site Localization and Thermodynamic Profiling. - Florian B. Hinz; Matthew R. Masters; Julia T. Nguyen; Amr H. Mahmoud; Markus A. Lill (J. Chem. Inf. Model., 2025)

An analysis of the modality and flexibility of the inverse stereographic normal distribution. - Florian B. Hinz; Amr H. Mahmoud; Markus A. Lill (Stat. Comput., 2025)

Neural SHAKE: geometric constraints in neural differential equations. - Justin S. Diamond; Markus A. Lill (J. Cheminformatics, 2025)