Five distinct strong hydrogen-bonding interactions of four kinds (N-H...Cl, N-H...O, O-H...N, and O-H...Cl) connect molecules of the title compound, C(9)H(18)N(3)(+).Cl(-).H(2)O, in the crystal structure into corrugated sheets stacked along the a axis. The intermolecular interactions are efficiently described in terms of the first- through fifth-level graph sets. A two-dimensional constructor graph helps visualize the supramolecular assembly.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1107/S0108270107031952 | DOI Listing |
Phys Rev E
November 2024
School of Science, Constructor University Bremen, Bremen, Germany.
We investigate the influence of the network topology on the asymptotic dynamical patterns, attractors, in a general model of excitable dynamics on signed directed graphs. In this framework, network topology manifests itself as an interplay of positive and negative feedback loops. A small change in a feedback loop, by addition or removal of edges in the graph, can drastically change the dynamical patterns in the network, characterized by the appearance and disappearance of attractors from the attractor space of the network.
View Article and Find Full Text PDFJ Chem Inf Model
April 2024
Tetra-d, Rheinweg 9, Schaffhausen 8200, Switzerland.
Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of binding constants. Building on recent advancements in graph neural networks, we present graphLambda for protein-ligand binding affinity prediction, which utilizes graph convolutional, attention, and isomorphism blocks to enhance the predictive capabilities.
View Article and Find Full Text PDFChaos
August 2023
School of Science, Constructor University Bremen, 28759 Bremen, Germany.
Stylized models of dynamical processes on graphs allow us to explore the relationships between network architecture and dynamics, a topic of relevance in a range of disciplines. One strategy is to translate dynamical observations into pairwise relationships of nodes, often called functional connectivity (FC), and quantitatively compare them with network architecture or structural connectivity (SC). Here, we start from the observation that for coupled logistic maps, SC/FC relationships vary strongly with coupling strength.
View Article and Find Full Text PDFPhys Rev E
July 2023
Department of Life Sciences and Chemistry, Constructor University, D-28759 Bremen, Germany.
How the architecture of gene regulatory networks shapes gene expression patterns is an open question, which has been approached from a multitude of angles. The dominant strategy has been to identify nonrandom features in these networks and then argue for the function of these features using mechanistic modeling. Here we establish the foundation of an alternative approach by studying the correlation of network eigenvectors with synthetic gene expression data simulated with a basic and popular model of gene expression dynamics: Boolean threshold dynamics in signed directed graphs.
View Article and Find Full Text PDFJ Mol Graph Model
September 2023
QingGong College, North China University of Science and Technology, TangShan, Hebei, 064000, China.
Molecular properties prediction and new material discovery are significant for the pharmaceutical industry, food, chemistry, and other fields. The popular methods are theoretical mechanism calculation and machine learning. There is a deviation between the theoretical mechanism calculation results and the experimental data.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!