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Graph Networks for Molecular Design [PDF]

Machine Learning: Science and Technology, 2020
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 a single bond at a time ...
Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, Esben Jannik Bjerrum   +6 more
openaire   +3 more sources

Prediction of Drug-Drug Interaction Using an Attention-Based Graph Neural Network on Drug Molecular Graphs

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
doaj   +3 more sources

Compressed graph representation for scalable molecular graph generation. [PDF]

J Cheminform, 2020
AbstractRecently, 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 of deep learning-based molecular graph generation to large molecules with many heavy atoms.
Kwon Y, Lee D, Choi YS, Shin K, Kang S.
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

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

Molecular graph contrastive learning with line graph

Pattern Recognition
Trapped 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.
Bowen Shi, Junran Wu, Shangzhe Li
exaly   +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 ...
Jonas, Verhellen, Verhellen, Jonas
openaire   +4 more sources

Knowledge-aware contrastive heterogeneous molecular graph learning. [PDF]

PLoS Comput Biol
Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability to integrate external knowledge ...
Chen M, Wu J, Pan S, Lin F, Du B, Gong X, Hu W.   +6 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