@article{SchmidtEhrenbergBaumHege2001, author = {Schmidt-Ehrenberg, Johannes and Baum, Daniel and Hege, Hans-Christian}, title = {Visually stunning - Molecular conformations}, volume = {23}, journal = {The Biochemist}, number = {5}, pages = {22 -- 26}, year = {2001}, language = {en} } @inproceedings{SchmidtEhrenbergBaumHege2002, author = {Schmidt-Ehrenberg, Johannes and Baum, Daniel and Hege, Hans-Christian}, title = {Visualizing Dynamic Molecular Conformations}, booktitle = {Proceedings of IEEE Visualization 2002}, editor = {J. Moorhead, Robert and Gross, Markus and I. Joy, Kenneth}, publisher = {IEEE Computer Society Press}, address = {Boston MA, USA}, doi = {10.1109/VISUAL.2002.1183780}, pages = {235 -- 242}, year = {2002}, language = {en} } @phdthesis{SchmidtEhrenberg2008, author = {Schmidt-Ehrenberg, Johannes}, title = {Analysis and Visualization of Molecular Conformations}, year = {2008}, language = {en} } @misc{CordesWeberSchmidtEhrenberg2002, author = {Cordes, Frank and Weber, Marcus and Schmidt-Ehrenberg, Johannes}, title = {Metastable Conformations via successive Perron-Cluster Cluster Analysis of dihedrals}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-7074}, number = {02-40}, year = {2002}, abstract = {Decomposition of the high dimensional conformational space of bio-molecules into metastable subsets is used for data reduction of long molecular trajectories in order to facilitate chemical analysis and to improve convergence of simulations within these subsets. The metastability is identified by the Perron-cluster cluster analysis of a Markov process that generates the thermodynamic distribution. A necessary prerequisite of this analysis is the discretization of the conformational space. A combinatorial approach via discretization of each degree of freedom will end in the so called ''curse of dimension''. In the following paper we analyze Hybrid Monte Carlo simulations of small, drug-like biomolecules and focus on the dihedral degrees of freedom as indicators of conformational changes. To avoid the ''curse of dimension'', the projection of the underlying Markov operator on each dihedral is analyzed according to its metastability. In each decomposition step of a recursive procedure, those significant dihedrals, which indicate high metastability, are used for further decomposition. The procedure is introduced as part of a hierarchical protocol of simulations at different temperatures. The convergence of simulations within metastable subsets is used as an ''a posteriori'' criterion for a successful identification of metastability. All results are presented with the visualization program AmiraMol.}, language = {en} } @misc{MayEisenhardtSchmidtEhrenbergetal.2003, author = {May, Andreas and Eisenhardt, Steffen and Schmidt-Ehrenberg, Johannes and Cordes, Frank}, title = {Rigid body docking for Virtual Screening}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-7690}, number = {03-47}, year = {2003}, abstract = {A recently developed algorithm allows Rigid Body Docking of ligands to proteins, regardless of the accessibility and location of the binding site. The Docking procedure is divided into three subsequent optimization phases, two of which utilize rigid body dynamics. The last one is applied with the ligand already positioned inside the binding pocket and accounts for full flexibility. Initially, a combination of geometrical and force-field based methods is used as a Coarse Docking strategy, considering only Lennard-Jones interactions between the target and pharmaceutically relevant atoms or functional groups. The protein is subjected to a Hot Spot Analysis, which reveals points of high affinity in the protein environment towards these groups. The hot spots are distributed into different subsets according to their group affiliation. The ligand is described as a complementary point set, consisting of the same subsets. Both sets are matched in \$\mathrm{I\!R}^{3}\$, by superimposing members of the same subsets. In the first instance, steric inhibition is nearly neglected, preventing the system's trajectory from trapping in local minima and thus from finding false positive solutions. Hence the exact location of the binding site can be determined fast and reliably without any additional information. Subsequently, errors resulting from approximations are minimized via finetuning, this time considering both Lennard-Jones and Coulomb forces. Finally, the potential energy of the whole complex is minimized. In a first evaluation, results are rated by a reduced scoring function considering only noncovalent interaction energies. Exemplary Screening results will be given for specific ligands.}, language = {en} } @misc{HorenkoSchmidtEhrenbergSchuette2006, author = {Horenko, Illia and Schmidt-Ehrenberg, Johannes and Sch{\"u}tte, Christof}, title = {Set-oriented dimension reduction: Localizing principal component analysis via hidden Markov models}, volume = {4216}, journal = {Computational Life Sciences II}, publisher = {Springer}, pages = {98 -- 115}, year = {2006}, language = {en} } @inproceedings{BaumHege2006, author = {Baum, Daniel and Hege, Hans-Christian}, title = {A Point-matching based algorithm for 3D surface alignment of drug-sized molecules}, volume = {4216}, booktitle = {Computational Life Sciences II, Second International Symposium, CompLife 2006, Cambridge (UK), Sept. 2006}, publisher = {Springer}, doi = {10.1007/11875741_18}, pages = {183 -- 193}, year = {2006}, language = {en} } @inproceedings{Baum2005, author = {Baum, Daniel}, title = {Multiple semi-flexible 3D superposition of drug-sized molecules}, volume = {3695}, booktitle = {Computational Life Sciences: First International Symposium, CompLife 2005}, publisher = {Springer}, address = {Konstanz, Germany}, doi = {10.1007/11560500_18}, pages = {198 -- 207}, year = {2005}, language = {en} } @article{HermannPohlTremblayetal.2016, author = {Hermann, Gunter and Pohl, Vincent and Tremblay, Jean Christophe and Paulus, Beate and Hege, Hans-Christian and Schild, Axel}, title = {ORBKIT - A modular Python toolbox for cross-platform post-processing of quantum chemical wavefunction data}, volume = {37}, journal = {Journal of Computational Chemistry}, number = {16}, doi = {10.1002/jcc.24358}, pages = {1511 -- 1520}, year = {2016}, abstract = {ORBKIT is a toolbox for postprocessing electronic structure calculations based on a highly modular and portable Python architecture. The program allows computing a multitude of electronic properties of molecular systems on arbitrary spatial grids from the basis set representation of its electronic wave function, as well as several grid-independent properties. The required data can be extracted directly from the standard output of a large number of quantum chemistry programs. ORBKIT can be used as a standalone program to determine standard quantities, for example, the electron density, molecular orbitals, and derivatives thereof. The cornerstone of ORBKIT is its modular structure. The existing basic functions can be arranged in an individual way and can be easily extended by user-written modules to determine any other derived quantity. ORBKIT offers multiple output formats that can be processed by common visualization tools (VMD, Molden, etc.). Additionally, ORBKIT offers routines to order molecular orbitals computed at different nuclear configurations according to their electronic character and to interpolate the wavefunction between these configurations. The program is open-source under GNU-LGPLv3 license and freely available at https://github.com/orbkit/orbkit/. This article provides an overview of ORBKIT with particular focus on its capabilities and applicability, and includes several example calculations.}, language = {en} } @phdthesis{Baum2007, author = {Baum, Daniel}, title = {A Point-Based Algorithm for Multiple 3D Surface Alignment of Drug-Sized Molecules}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:188-fudissthesis000000002759-2}, school = {Freie Universit{\"a}t Berlin}, year = {2007}, abstract = {One crucial step in virtual drug design is the identification of new lead structures with respect to a pharmacological target molecule. The search for new lead structures is often done with the help of a pharmacophore, which carries the essential structural as well as physico-chemical properties that a molecule needs to have in order to bind to the target molecule. In the absence of the target molecule, such a pharmacophore can be established by comparison of a set of active compounds. In order to identify their common features,a multiple alignment of all or most of the active compounds is necessary. Moreover, since the "outer shape" of the molecules plays a major role in the interaction between drug and target, an alignment algorithm aiming at the identification of common binding properties needs to consider the molecule's "outer shape", which can be approximated by the solvent excluded surface. In this thesis, we present a new approach to molecular surface alignment based on a discrete representation of shape as well as physico-chemical properties by points distributed on the solvent excluded surface. We propose a new method to distribute points regularly on a surface w.r.t. a smoothly varying point density given on that surface. Since the point distribution algorithm is not restricted to molecular surfaces, it might also be of interest for other applications. For the computation of pairwise surface alignments, we extend an existing point matching scheme to surface points, and we develop an efficient data structure speeding up the computation by a factor of three. Moreover, we present an approach to compute multiple alignments from pairwise alignments, which is able to handle a large number of surface points. All algorithms are evaluated on two sets of molecules: eight thermolysin inhibitors and seven HIV-1 protease inhibitors. Finally, we compare the results obtained from surface alignment with the results obtained by applying an atom alignment approach.}, language = {en} } @article{SterzikLichtenbergKroneetal.2023, author = {Sterzik, Anna and Lichtenberg, Nils and Krone, Michael and Baum, Daniel and Cunningham, Douglas W. and Lawonn, Kai}, title = {Enhancing molecular visualization: Perceptual evaluation of line variables with application to uncertainty visualization}, volume = {114}, journal = {Computers \& Graphics}, doi = {10.1016/j.cag.2023.06.006}, pages = {401 -- 413}, year = {2023}, abstract = {Data are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Specifically, the effective communication of the uncertainty can influence the interpretation of the data and the user's trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Thus, we conducted two perceptual studies to determine the suitability of the line variables blur, dashing, grayscale, sketchiness, and width for distinguishing several values in molecular visualizations. While our work was motivated by uncertainty visualization, our techniques and study results also apply to other types of scalar data.}, language = {en} } @article{SterzikKroneBaumetal.2025, author = {Sterzik, Anna and Krone, Michael and Baum, Daniel and Cunningham, Douglas W. and Lawonn, Kai}, title = {Uncertainty Visualization for Biomolecular Structures: An Empirical Evaluation}, volume = {31}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {12}, doi = {10.1109/TVCG.2025.3596385}, pages = {10296 -- 10310}, year = {2025}, abstract = {Uncertainty is an intrinsic property of almost all data, regardless of the data being measured, simulated, or generated. It can significantly influence the results and reliability of subsequent analysis steps. Clearly communicating uncertainties is crucial for informed decision-making and understanding, especially in biomolecular data, where uncertainty is often difficult to infer. Uncertainty visualization (UV) is a powerful tool for this purpose. However, previously proposed UV methods lack sufficient empirical evaluation. We collected and categorized visualization methods for portraying positional uncertainty in biomolecular structures. We then organized the methods into metaphorical groups and extracted nine representatives: color, clouds, ensemble, hulls, sausages, contours, texture, waves, and noise. We assessed their strengths and weaknesses in a twofold approach: expert assessments with six domain experts and three perceptual evaluations involving 1,756 participants. Through the expert assessments, we aimed to highlight the advantages and limitations of the individual methods for the application domain and discussed areas for necessary improvements. Through the perceptual evaluation, we investigated whether the visualizations are intuitively associated with uncertainty and whether the directionality of the mapping is perceived as intended. We also assessed the accuracy of inferring uncertainty values from the visualizations. Based on our results, we judged the appropriateness of the metaphors for encoding uncertainty and suggest further areas for improvement.}, language = {en} }