Results 11 to 20 of about 455,903 (307)
Compressed graph representation for scalable molecular graph generation [PDF]
Journal of Cheminformatics, 2020 Recently, deep learning has been successfully applied to molecular graph generation. Nevertheless, mitigating the computational complexity, which increases with the number of nodes in a graph, has been a major challenge. This has hindered the application
Youngchun Kwon +4 more
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Group graph: a molecular graph representation with enhanced performance, efficiency and interpretability [PDF]
Journal of CheminformaticsThe exploration of chemical space holds promise for developing influential chemical entities. Molecular representations, which reflect features of molecular structure in silico, assist in navigating chemical space appropriately.
Piao-Yang Cao +5 more
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Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation [PDF]
Journal of Cheminformatics, 2019 With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation.
Youngchun Kwon +5 more
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Molecular graph contrastive learning with line graph
Pattern RecognitionTrapped by the label scarcity in molecular property prediction and drug design, graph contrastive learning (GCL) came forward. Leading contrastive learning works show two kinds of view generators, that is, random or learnable data corruption and domain knowledge incorporation.
Xueyuan Chen, Shangzhe Li, Ruomei Liu
exaly +3 more sources
A Multimodal Representation Learning Framework for Molecular Graph and NMR Spectrum Alignment. [PDF]
Entropy (Basel)Li X, Wang X, Liu ZM, Liu JB, Huang X.
europepmc +3 more sources
The high-energy band in the photoelectron spectrum of alkanes and its dependence on molecular structure [PDF]
Journal of the Serbian Chemical Society, 1999 In the model for the ionization energies of the C2s-electrons in saturated hydrocarbons, put forward by Heilbronner et al., the energy levels are calculated as eigenvalues of the line graph of the hydrogen-filled molecular graph. It is now shown
Gutman Ivan +3 more
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Degree-Based Graph Entropy in Structure–Property Modeling
Entropy, 2023 Graph entropy plays an essential role in interpreting the structural information and complexity measure of a network. Let G be a graph of order n. Suppose dG(vi) is degree of the vertex vi for each i=1,2,…,n.
Sourav Mondal, Kinkar Chandra Das
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Molecules, 2022 The treatment of complex diseases by using multiple drugs has become popular. However, drug-drug interactions (DDI) may give rise to the risk of unanticipated adverse effects and even unknown toxicity.
Yue-Hua Feng, Shao-Wu Zhang
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Graph Networks for Molecular Design [PDF]
Machine Learning: Science and Technology, 2020 Abstract Deep learning methods applied to chemistry can be used to accelerate the discovery of new molecules. This work introduces GraphINVENT, a platform developed for graph-based molecular design using graph neural networks (GNNs). GraphINVENT uses a tiered deep neural network architecture to probabilistically generate new molecules
Rocío Mercado +6 more
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Системный анализ и прикладная информатика, 2023 The user's interface «Nomenclature Generator» for extraction of the chemical structure information from the systematic name of organic compound represented according to IUPAC nomenclature is developed at the All-Russian Institute for Scientific and ...
L. A. Grigoryan
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