Results 21 to 30 of about 908,779 (266)

Molecular cartooning with knowledge graphs

open access: yesFrontiers in Bioinformatics, 2022
Molecular “cartoons,” such as pathway diagrams, provide a visual summary of biomedical research results and hypotheses. Their ubiquitous appearance within the literature indicates their universal application in mechanistic communication. A recent survey of pathway diagrams identified 64,643 pathway figures published between 1995 and 2019 with 1,112,551
Brook E. Santangelo   +3 more
openaire   +3 more sources

Linear representation of graphs: applications to molecular graphs

open access: yesMatch - Communications in Mathematical and in Computer Chemistry, 2022
Abstract In this article we build a linear representation starting from a multigraph; this allows us to give an algebraic view of the multi-graph we are studying. We show that two isomorphic multi-graphs give equivalent representations ; conversely two equivalent representations give isomorphic multigraphs.
Ashrafi, Ali   +2 more
openaire   +4 more sources

Molecular similarity for machine learning in drug development : poster presentation [PDF]

open access: yes, 2008
Poster presentation In pharmaceutical research and drug development, machine learning methods play an important role in virtual screening and ADME/Tox prediction.
Proschak, Ewgenij   +2 more
core   +1 more source

Constructing NSSD Molecular Graphs

open access: yesCroatica Chemica Acta, 2016
A graph is said to be non-singular if it has no eigenvalue equal to zero; otherwise it is singular. Molecular graphs that are non-singular and also have the property that all subgraphs of them obtained by deleting a single vertex are themselves singular, known as NSSD graphs, are of importance in the theory of molecular π-electron conductors; NSSD ...
Gutman, Ivan   +3 more
openaire   +4 more sources

Graph-based linear scaling electronic structure theory [PDF]

open access: yes, 2016
We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including ...
Bock, Nicolas   +10 more
core   +1 more source

Chemical applications of the Laplacian spectrum. VI On the largest Laplacian eigenvalue of alkanes [PDF]

open access: yesJournal of the Serbian Chemical Society, 2002
The largest Lapacian eigenvalue µ1 of the molecular graph is a quantity important in the theory of the photoelectron spectra of saturated hydrocarbons.
Gutman Ivan   +2 more
doaj   +1 more source

A Paradigmatic Approach to Find the Valency-Based K-Banhatti and Redefined Zagreb Entropy for Niobium Oxide and a Metal–Organic Framework

open access: yesMolecules, 2022
Entropy is a thermodynamic function in chemistry that reflects the randomness and disorder of molecules in a particular system or process based on the number of alternative configurations accessible to them.
Muhammad Usman Ghani   +5 more
doaj   +1 more source

Stochastic dynamics of model proteins on a directed graph [PDF]

open access: yes, 2008
A method for reconstructing the energy landscape of simple polypeptidic chains is described. We show that we can construct an equivalent representation of the energy landscape by a suitable directed graph. Its topological and dynamical features are shown
Alessandro Torcini   +12 more
core   +1 more source

Testing the quality of molecular structure descriptors. Vertex-degree-based topological indices [PDF]

open access: yesJournal of the Serbian Chemical Society, 2013
The correlation ability of 20 vertex-degree-based topological indices, occurring in the chemical literature, is tested for the case of standard heats of formation and normal boiling points of octane isomers. It is found that the correlation ability of
Gutman Ivan, Tošović Jelena
doaj   +1 more source

Adversarial Learned Molecular Graph Inference and Generation

open access: yes, 2020
Recent methods for generating novel molecules use graph representations of molecules and employ various forms of graph convolutional neural networks for inference.
Pölsterl, Sebastian   +1 more
core   +1 more source

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