Markus A. Lill

35 papers C 1Journal 33Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Accelerated Hydration Site Localization and Thermodynamic Profiling. J. Chem. Inf. Model. Florian B. Hinz, Matthew R. Masters, Julia T. Nguyen, Amr H. Mahmoud, Markus A. Lill
2025	J	`jnl`	An analysis of the modality and flexibility of the inverse stereographic normal distribution. Stat. Comput. Florian B. Hinz, Amr H. Mahmoud, Markus A. Lill
2025	J	`jnl`	Diffusion Models are Molecular Dynamics Simulators. CoRR Justin S. Diamond, Markus A. Lill
2025	J	`jnl`	Neural SHAKE: geometric constraints in neural differential equations. J. Cheminformatics Justin S. Diamond, Markus A. Lill
2024	J	`jnl`	Accelerated Hydration Site Localization and Thermodynamic Profiling. CoRR Florian B. Hinz, Matthew R. Masters, Julia N. Kieu, Amr H. Mahmoud, Markus A. Lill
2024	—	`conf`	Neural SHAKE: Geometric Constraints in Graph Generative Models. ICANN (10) Justin S. Diamond, Markus A. Lill
2023	J	`jnl`	Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbation. CoRR Soo Jung Lee, Amr H. Mahmoud, Markus A. Lill
2023	J	`jnl`	Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility. J. Chem. Inf. Model. Matthew R. Masters, Amr H. Mahmoud, Yao Wei, Markus A. Lill
2023	J	`jnl`	Efficient virtual high-content screening using a distance-aware transformer model. J. Cheminformatics Manuel S. Sellner, Amr H. Mahmoud, Markus A. Lill
2023	J	`jnl`	Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes. CoRR Justin S. Diamond, Markus A. Lill
2023	J	`jnl`	Improving Protein-peptide Interface Predictions in the Low Data Regime. CoRR Justin S. Diamond, Markus A. Lill
2023	J	`jnl`	Prediction of molecular field points using SE(3)-transformer model. Mach. Learn. Sci. Technol. Florian B. Hinz, Amr H. Mahmoud, Markus A. Lill
2022	J	`jnl`	Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation. J. Chem. Inf. Model. Amr H. Mahmoud, Matthew Masters, Soo Jung Lee, Markus A. Lill
2022	C	`conf`	Influence of Assimilation Effects on Recommender Systems. ICIS Markus A. Lill, Martin Spann
2021	J	`jnl`	Computational Assessment of Combination Therapy of Androgen Receptor-Targeting Compounds. J. Chem. Inf. Model. André Fischer, Florian Häuptli, Markus A. Lill, Martin Smiesko
2021	J	`jnl`	Conformational Changes of Thyroid Receptors in Response to Antagonists. J. Chem. Inf. Model. André Fischer, Gabriela Frehner, Markus A. Lill, Martin Smiesko
2020	J	`jnl`	On-the-fly Prediction of Protein Hydration Densities and Free Energies using Deep Learning. CoRR Ahmadreza Ghanbarpour, Amr H. Mahmoud, Markus A. Lill
2019	J	`jnl`	Modeling of Halogen-Protein Interactions in Co-Solvent Molecular Dynamics Simulations. J. Chem. Inf. Model. Ying Yang, Amr H. Mahmoud, Markus A. Lill
2018	J	`jnl`	Efficient and Accurate Hydration Site Profiling for Enclosed Binding Sites. J. Chem. Inf. Model. Matthew R. Masters, Amr H. Mahmoud, Ying Yang, Markus A. Lill
2017	J	`jnl`	Molecular Modeling Evaluation of the Enantiomers of a Novel Adenylyl Cyclase 2 Inhibitor. J. Chem. Inf. Model. Neha Rana, Jason M. Conley, Monica Soto-Velasquez, Francisco León, Stephen J. Cutler, Val J. Watts, Markus A. Lill
2016	J	`jnl`	Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations. J. Comput. Chem. Laura J. Kingsley, Juan Esquivel-Rodríguez, Ying Yang, Daisuke Kihara, Markus A. Lill
2014	J	`jnl`	Analysis of Factors Influencing Hydration Site Prediction Based on Molecular Dynamics Simulations. J. Chem. Inf. Model. Ying Yang, Bingjie Hu, Markus A. Lill
2014	J	`jnl`	Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction? J. Bioinform. Comput. Biol. Hamed Tabatabaei Ghomi, Jared J. Thompson, Markus A. Lill
2014	J	`jnl`	Including ligand-induced protein flexibility into protein tunnel prediction. J. Comput. Chem. Laura J. Kingsley, Markus A. Lill
2014	J	`jnl`	IterTunnel; a method for predicting and evaluating ligand EgressTunnels in proteins with buried active sites. J. Cheminformatics Laura J. Kingsley, Markus A. Lill
2014	J	`jnl`	PharmDock: a pharmacophore-based docking program. J. Cheminformatics Bingjie Hu, Markus A. Lill
2014	J	`jnl`	WATsite: Hydration site prediction program with PyMOL interface. J. Comput. Chem. Bingjie Hu, Markus A. Lill
2013	J	`jnl`	Exploring the Potential of Protein-Based Pharmacophore Models in Ligand Pose Prediction and Ranking. J. Chem. Inf. Model. Bingjie Hu, Markus A. Lill
2012	J	`jnl`	Protein Pharmacophore Selection Using Hydration-Site Analysis. J. Chem. Inf. Model. Bingjie Hu, Markus A. Lill
2012	J	`jnl`	Utilizing Experimental Data for Reducing Ensemble Size in Flexible-Protein Docking. J. Chem. Inf. Model. Mengang Xu, Markus A. Lill
2011	J	`jnl`	Computer-aided drug design platform using PyMOL. J. Comput. Aided Mol. Des. Markus A. Lill, Matthew L. Danielson
2011	J	`jnl`	Significant Enhancement of Docking Sensitivity Using Implicit Ligand Sampling. J. Chem. Inf. Model. Mengang Xu, Markus A. Lill
2011	J	`jnl`	Solvent Interaction Energy Calculations on Molecular Dynamics Trajectories: Increasing the Efficiency Using Systematic Frame Selection. J. Chem. Inf. Model. Markus A. Lill, Jared J. Thompson
2009	J	`jnl`	Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR. PLoS Comput. Biol. Sean Ekins, Sandhya Kortagere, Manisha Iyer, Erica J. Reschly, Markus A. Lill, Matthew R. Redinbo, Matthew D. Krasowski
2006	J	`jnl`	Combining 4D Pharmacophore Generation and Multidimensional QSAR: Modeling Ligand Binding to the Bradykinin B J. Chem. Inf. Model. Markus A. Lill, Angelo Vedani