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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, Dongseon Lee, Youn-Suk Choi, Kyoham Shin, Seokho Kang   +4 more
doaj   +5 more sources

Graph-Based Molecular Pareto Optimisation [PDF]

Chemical Science, 2022
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning models requires significant, if not ...
Jonas Verhellen
openalex   +4 more sources

Group graph: a molecular graph representation with enhanced performance, efficiency and interpretability [PDF]

Journal of Cheminformatics
The 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, Yang He, Ming-Yang Cui, Xiao-Min Zhang, Qingye Zhang, Hong-Yu Zhang   +5 more
doaj   +2 more sources

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization [PDF]

, 2018
We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph.
Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola   +3 more
openalex   +4 more sources

Molecular graph convolutions: moving beyond fingerprints. [PDF]

J Comput Aided Mol Des, 2016
See "Version information ...
Kearnes S, McCloskey K, Berndl M, Pande V, Riley P.   +4 more
europepmc   +5 more sources

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, Jiho Yoo, Youn-Suk Choi, Won-Joon Son, Dongseon Lee, Seokho Kang   +5 more
doaj   +2 more sources

Application of deep metric learning to molecular graph similarity. [PDF]

J Cheminform, 2022
Coupry DE, Pogány P.
europepmc   +3 more sources

Improving hERG Cardiotoxicity Prediction via SMILES Transformer, Molecular Fingerprints, and Layer-Wise Self-Adaptive Graph Attention Network. [PDF]

ACS Omega
Beidja C, Bousselham A, En-Naimani Z, Mestari M.   +3 more
europepmc   +2 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, Gineityte Viktorija, Lepović Mirko, Petrović Miroslav   +3 more
doaj   +3 more sources

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
doaj   +1 more source