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Markus A. Lill
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2020 – today
- 2024
- [c2]Justin Diamond, Markus A. Lill:
Neural SHAKE: Geometric Constraints in Graph Generative Models. ICANN (10) 2024: 43-57 - 2023
- [j24]Manuel S. Sellner, Amr H. Mahmoud, Markus A. Lill
:
Efficient virtual high-content screening using a distance-aware transformer model. J. Cheminformatics 15(1): 18 (2023) - [j23]Matthew R. Masters, Amr H. Mahmoud, Yao Wei, Markus A. Lill
Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility. J. Chem. Inf. Model. 63(6): 1695-1707 (2023) - [j22]Florian B. Hinz, Amr H. Mahmoud, Markus A. Lill
Prediction of molecular field points using SE(3)-transformer model. Mach. Learn. Sci. Technol. 4(3): 35016 (2023) - [i4]Soo Jung Lee, Amr H. Mahmoud, Markus A. Lill
Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbation. CoRR abs/2302.11855 (2023) - [i3]Justin Diamond, Markus A. Lill:
Improving Protein-peptide Interface Predictions in the Low Data Regime. CoRR abs/2306.00557 (2023) - [i2]Justin Diamond, Markus A. Lill:
Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes. CoRR abs/2307.04493 (2023) - 2022
- [j21]Amr H. Mahmoud, Matthew Masters, Soo Jung Lee, Markus A. Lill
Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation. J. Chem. Inf. Model. 62(7): 1602-1617 (2022) - [c1]Markus A. Lill, Martin Spann:
Influence of Assimilation Effects on Recommender Systems. ICIS 2022 - 2021
- [j20]André Fischer
Computational Assessment of Combination Therapy of Androgen Receptor-Targeting Compounds. J. Chem. Inf. Model. 61(2): 1001-1009 (2021) - [j19]André Fischer
Conformational Changes of Thyroid Receptors in Response to Antagonists. J. Chem. Inf. Model. 61(2): 1010-1019 (2021) - 2020
- [i1]Ahmadreza Ghanbarpour
On-the-fly Prediction of Protein Hydration Densities and Free Energies using Deep Learning. CoRR abs/2001.02201 (2020)
2010 – 2019
- 2019
- [j18]Ying Yang, Amr H. Mahmoud, Markus A. Lill
Modeling of Halogen-Protein Interactions in Co-Solvent Molecular Dynamics Simulations. J. Chem. Inf. Model. 59(1): 38-42 (2019) - 2018
- [j17]Matthew R. Masters, Amr H. Mahmoud, Ying Yang, Markus A. Lill
Efficient and Accurate Hydration Site Profiling for Enclosed Binding Sites. J. Chem. Inf. Model. 58(11): 2183-2188 (2018) - 2017
- [j16]Neha Rana
Molecular Modeling Evaluation of the Enantiomers of a Novel Adenylyl Cyclase 2 Inhibitor. J. Chem. Inf. Model. 57(2): 322-334 (2017) - 2016
- [j15]Laura J. Kingsley
Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations. J. Comput. Chem. 37(20): 1861-1865 (2016) - 2014
- [j14]Hamed Tabatabaei Ghomi, Jared J. Thompson, Markus A. Lill
Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction? J. Bioinform. Comput. Biol. 12(5) (2014) - [j13]Bingjie Hu, Markus A. Lill
WATsite: Hydration site prediction program with PyMOL interface. J. Comput. Chem. 35(16): 1255-1260 (2014) - [j12]Laura J. Kingsley
Including ligand-induced protein flexibility into protein tunnel prediction. J. Comput. Chem. 35(24): 1748-1756 (2014) - [j11]Bingjie Hu, Markus A. Lill
PharmDock: a pharmacophore-based docking program. J. Cheminformatics 6(1): 14 (2014) - [j10]Laura J. Kingsley
IterTunnel; a method for predicting and evaluating ligand EgressTunnels in proteins with buried active sites. J. Cheminformatics 6(S-1): 62 (2014) - [j9]Ying Yang, Bingjie Hu, Markus A. Lill
Analysis of Factors Influencing Hydration Site Prediction Based on Molecular Dynamics Simulations. J. Chem. Inf. Model. 54(10): 2987-2995 (2014) - 2013
- [j8]Bingjie Hu, Markus A. Lill
Exploring the Potential of Protein-Based Pharmacophore Models in Ligand Pose Prediction and Ranking. J. Chem. Inf. Model. 53(5): 1179-1190 (2013) - 2012
- [j7]Mengang Xu, Markus A. Lill
Utilizing Experimental Data for Reducing Ensemble Size in Flexible-Protein Docking. J. Chem. Inf. Model. 52(1): 187-198 (2012) - [j6]Bingjie Hu, Markus A. Lill
Protein Pharmacophore Selection Using Hydration-Site Analysis. J. Chem. Inf. Model. 52(4): 1046-1060 (2012) - 2011
- [j5]Markus A. Lill
Computer-aided drug design platform using PyMOL. J. Comput. Aided Mol. Des. 25(1): 13-19 (2011) - [j4]Mengang Xu, Markus A. Lill
Significant Enhancement of Docking Sensitivity Using Implicit Ligand Sampling. J. Chem. Inf. Model. 51(3): 693-706 (2011) - [j3]Markus A. Lill
Solvent Interaction Energy Calculations on Molecular Dynamics Trajectories: Increasing the Efficiency Using Systematic Frame Selection. J. Chem. Inf. Model. 51(10): 2680-2689 (2011)
2000 – 2009
- 2009
- [j2]Sean Ekins
Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR. PLoS Comput. Biol. 5(12) (2009) - 2006
- [j1]Markus A. Lill
Combining 4D Pharmacophore Generation and Multidimensional QSAR: Modeling Ligand Binding to the Bradykinin B2 Receptor. J. Chem. Inf. Model. 46(5): 2135-2145 (2006)
Coauthor Index
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