Markus A. Lill

According to our database, Markus A. Lill authored at least 34 papers between 2006 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Neural SHAKE: geometric constraints in neural differential equations.

Justin S. Diamond, Markus A. Lill

J. Cheminformatics, December, 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., April, 2025

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

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

Neural SHAKE: Geometric Constraints in Graph Generative Models.

Justin S. Diamond, Markus A. Lill

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2024, 2024

2023

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

Manuel S. Sellner, Amr H. Mahmoud, Markus A. Lill

J. Cheminformatics, December, 2023

Prediction of molecular field points using SE(3)-transformer model.

Florian B. Hinz, Amr H. Mahmoud, Markus A. Lill

Mach. Learn. Sci. Technol., September, 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., March, 2023

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

Justin S. Diamond, Markus A. Lill

CoRR, 2023

Improving Protein-peptide Interface Predictions in the Low Data Regime.

Justin S. Diamond, Markus A. Lill

CoRR, 2023

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

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

Influence of Assimilation Effects on Recommender Systems.

Markus A. Lill, Martin Spann

Proceedings of the 43rd International Conference on Information Systems, 2022

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

Conformational Changes of Thyroid Receptors in Response to Antagonists.

André Fischer, Gabriela Frehner, Markus A. Lill, Martin Smiesko

J. Chem. Inf. Model., 2021

2020

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

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

CoRR, 2020

2019

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

Ying Yang, Amr H. Mahmoud, Markus A. Lill

J. Chem. Inf. Model., 2019

2018

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

2017

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

2016

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

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

IterTunnel; a method for predicting and evaluating ligand EgressTunnels in proteins with buried active sites.

Laura J. Kingsley, Markus A. Lill

J. Cheminformatics, 2014

PharmDock: a pharmacophore-based docking program.

Bingjie Hu, Markus A. Lill

J. Cheminformatics, 2014

Including ligand-induced protein flexibility into protein tunnel prediction.

Laura J. Kingsley, Markus A. Lill

J. Comput. Chem., 2014

WATsite: Hydration site prediction program with PyMOL interface.

Bingjie Hu, Markus A. Lill

J. Comput. Chem., 2014

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

2013

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

Bingjie Hu, Markus A. Lill

J. Chem. Inf. Model., 2013

2012

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

Mengang Xu, Markus A. Lill

J. Chem. Inf. Model., 2012

Protein Pharmacophore Selection Using Hydration-Site Analysis.

Bingjie Hu, Markus A. Lill

J. Chem. Inf. Model., 2012

2011

Significant Enhancement of Docking Sensitivity Using Implicit Ligand Sampling.

Mengang Xu, Markus A. Lill

J. Chem. Inf. Model., 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

Computer-aided drug design platform using PyMOL.

Markus A. Lill, Matthew L. Danielson

J. Comput. Aided Mol. Des., 2011

2009

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

2006

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