Markus A. Lill
Orcid: 0000-0003-3023-5188
According to our database1,
Markus A. Lill
authored at least 32 papers
between 2006 and 2025.
Collaborative distances:
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Bibliography
2025
An analysis of the modality and flexibility of the inverse stereographic normal distribution.
Stat. Comput., April, 2025
2024
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2024, 2024
2023
J. Cheminformatics, December, 2023
Mach. Learn. Sci. Technol., September, 2023
Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility.
J. Chem. Inf. Model., March, 2023
Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes.
CoRR, 2023
Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbation.
CoRR, 2023
2022
Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation.
J. Chem. Inf. Model., 2022
Proceedings of the 43rd International Conference on Information Systems, 2022
2021
Computational Assessment of Combination Therapy of Androgen Receptor-Targeting Compounds.
J. Chem. Inf. Model., 2021
J. Chem. Inf. Model., 2021
2020
On-the-fly Prediction of Protein Hydration Densities and Free Energies using Deep Learning.
CoRR, 2020
2019
Modeling of Halogen-Protein Interactions in Co-Solvent Molecular Dynamics Simulations.
J. Chem. Inf. Model., 2019
2018
J. Chem. Inf. Model., 2018
2017
Molecular Modeling Evaluation of the Enantiomers of a Novel Adenylyl Cyclase 2 Inhibitor.
J. Chem. Inf. Model., 2017
2016
Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations.
J. Comput. Chem., 2016
2014
Analysis of Factors Influencing Hydration Site Prediction Based on Molecular Dynamics Simulations.
J. Chem. Inf. Model., 2014
IterTunnel; a method for predicting and evaluating ligand EgressTunnels in proteins with buried active sites.
J. Cheminformatics, 2014
J. Comput. Chem., 2014
J. Comput. Chem., 2014
Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction?
J. Bioinform. Comput. Biol., 2014
2013
Exploring the Potential of Protein-Based Pharmacophore Models in Ligand Pose Prediction and Ranking.
J. Chem. Inf. Model., 2013
2012
J. Chem. Inf. Model., 2012
J. Chem. Inf. Model., 2012
2011
J. Chem. Inf. Model., 2011
Solvent Interaction Energy Calculations on Molecular Dynamics Trajectories: Increasing the Efficiency Using Systematic Frame Selection.
J. Chem. Inf. Model., 2011
2009
Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR.
PLoS Comput. Biol., 2009
2006
Combining 4D Pharmacophore Generation and Multidimensional QSAR: Modeling Ligand Binding to the Bradykinin B<sub>2</sub> Receptor.
J. Chem. Inf. Model., 2006