Graph Decipher: A transparent dual‐attention graph neural network to understand the message‐passing mechanism for the node classification
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22966
Abstract: Graph neural networks (GNNs) can be effectively applied to solve many real‐world problems across widely diverse fields. Their success is inseparable from the message‐passing mechanisms evolving over the years. However, current mechanisms treat all node… read more here.
Keywords: node classification; message passing; graph; graph neural ... See more keywords
Nation-wide human mobility prediction based on graph neural networks
Sign Up to like & getrecommendations! Published in 2021 at "Applied Intelligence"
DOI: 10.1007/s10489-021-02645-3
Abstract: Nowadays, the anticipation of human mobility flow has important applications in many domains ranging from urban planning to epidemiology. Because of the high predictability of human movements, numerous successful solutions to perform such forecasting have… read more here.
Keywords: mobility; graph neural; human mobility; nation wide ... See more keywords
The interplay between communities and homophily in semi-supervised classification using graph neural networks
Sign Up to like & getrecommendations! Published in 2021 at "Applied Network Science"
DOI: 10.1007/s41109-021-00423-1
Abstract: Graph Neural Networks (GNNs) are effective in many applications. Still, there is a limited understanding of the effect of common graph structures on the learning process of GNNs. To fill this gap, we study the… read more here.
Keywords: classification; neural networks; semi supervised; community ... See more keywords
SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks
Sign Up to like & getrecommendations! Published in 2020 at "SoftwareX"
DOI: 10.1016/j.softx.2020.100516
Abstract: Abstract SeMi (SEmantic Modeling machIne) is a tool to semi-automatically build large-scale Knowledge Graphs from structured sources such as CSV, JSON, and XML files. To achieve such a goal, SeMi builds the semantic models of… read more here.
Keywords: semi; modeling; knowledge graphs; graph neural ... See more keywords
Polymer Informatics at Scale with Multitask Graph Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "Chemistry of Materials"
DOI: 10.1021/acs.chemmater.2c02991
Abstract: Artificial intelligence-based methods are becoming increasingly effective at screening libraries of polymers down to a selection that is manageable for experimental inquiry. The vast majority of presently adopted approaches for polymer screening rely on handcrafted… read more here.
Keywords: informatics scale; neural networks; scale multitask; graph neural ... See more keywords
Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks
Sign Up to like & getrecommendations! Published in 2020 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.0c00416
Abstract: This work considers strategies to develop accurate and reliable graph neural networks (GNNs) for molecular property predictions. Prediction performance of GNNs is highly sensitive to the change in various parameters due to the inherent challenges… read more here.
Keywords: neural networks; study; graph neural; supervised learning ... See more keywords
XGraphBoost: Extracting Graph Neural Network-Based Features for a Better Prediction of Molecular Properties
Sign Up to like & getrecommendations! Published in 2021 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.0c01489
Abstract: Determining the properties of chemical molecules is essential for screening candidates similar to a specific drug. These candidate molecules are further evaluated for their target binding affinities, side effects, target missing probabilities, etc. Conventional machine… read more here.
Keywords: molecular properties; xgraphboost; graph neural; neural network ... See more keywords
Predicting Protein-Ligand Docking Structure with Graph Neural Network
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c00127
Abstract: Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy… read more here.
Keywords: neural network; graph neural; protein ligand;
Accurate Prediction of Aqueous Free Solvation Energies Using 3D Atomic Feature-Based Graph Neural Network with Transfer Learning
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c00260
Abstract: Graph neural network (GNN)-based deep learning (DL) models have been widely implemented to predict the experimental aqueous solvation free energy, while its prediction accuracy has reached a plateau partly due to the scarcity of available… read more here.
Keywords: neural network; solvation; prediction; graph neural ... See more keywords
Nucleophilicity Prediction Using Graph Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c00696
Abstract: The quantitative description between chemical reaction rates and nucleophilicity parameters plays a crucial role in organic chemistry. In this regard, the formula proposed by Mayr et al. and the constructed reactivity database are important representatives.… read more here.
Keywords: information; neural networks; nucleophilicity; prediction ... See more keywords
Explainable Solvation Free Energy Prediction Combining Graph Neural Networks with Chemical Intuition
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c01013
Abstract: The prediction of a molecule's solvation Gibbs free (ΔGsolv) energy in a given solvent is an important task which has traditionally been carried out via quantum chemical continuum methods or force field-based molecular simulations. Machine… read more here.
Keywords: neural networks; explainable solvation; energy; prediction ... See more keywords