Comprehensive Exploration of Target‐specific Ligands Using a Graph Convolution Neural Network
Sign Up to like & getrecommendations! Published in 2019 at "Molecular Informatics"
DOI: 10.1002/minf.201900095
Abstract: Machine learning approaches are widely used to evaluate ligand activities of chemical compounds toward potential target proteins. Especially, exploration of highly selective ligands is important for the development of new drugs with higher safety. One… read more here.
Keywords: convolution neural; graph convolution; exploration; target ... See more keywords
First Results in Leak Localization in Water Distribution Networks using Graph-Based Clustering and Deep Learning
Sign Up to like & getrecommendations! Published in 2020 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2020.12.1104
Abstract: Abstract This paper presents a methodology for the localization of leaks in water distribution networks (WDNs) by means of the combination of a deep learning (DL) approach and a graph-based clustering technique. A data set… read more here.
Keywords: based clustering; distribution networks; water distribution; graph based ... See more keywords
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2021.08.028
Abstract: The graph structure is a commonly used data storage mode, and it turns out that the low-dimensional embedded representation of nodes in the graph is extremely useful in various typical tasks, such as node classification,… read more here.
Keywords: representation; learning graphs; graphs using; representation learning ... See more keywords
embryoTox: Using Graph-Based Signatures to Predict the Teratogenicity of Small Molecules
Sign Up to like & getrecommendations! Published in 2023 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c00824
Abstract: Teratogenic drugs can lead to extreme fetal malformation and consequently critically influence the fetus's health, yet the teratogenic risks associated with most approved drugs are unknown. Here, we propose a novel predictive tool, embryoTox, which… read more here.
Keywords: based signatures; using graph; embryotox using; graph based ... See more keywords
Navigating the Fragrance Space Using Graph Generative Models and Predicting Odors
Sign Up to like & getrecommendations! Published in 2025 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.5c00209
Abstract: We explore a suite of generative modeling techniques to efficiently navigate and explore the complex landscapes of odor and the broader chemical space. Unlike traditional approaches, we not only generate molecules but also predict the… read more here.
Keywords: fragrance space; space; fragrance; navigating fragrance ... See more keywords
Using graph neural network and symbolic regression to model disordered systems
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-05205-8
Abstract: The key to modeling disordered systems lies in accurately simulating atomic trajectories, typically achieved through molecular dynamic (MD) simulation. The accuracy of MD simulations depends on the precision of the interatomic potential function, which dictates… read more here.
Keywords: graph neural; interatomic potential; network symbolic; disordered systems ... See more keywords
Enhanced IoT threat detection using Graph-Regularized neural networks optimized by Sea-Lion algorithm
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-10238-0
Abstract: The Internet of Things (IoT) has revolutionized business operations, but its interconnected nature introduces significant cyber security risks, including malware and software piracy that compromise sensitive data and organizational reputation. To address this challenge, we… read more here.
Keywords: detection using; graph regularized; iot threat; threat detection ... See more keywords
IoT-Based Android Malware Detection Using Graph Neural Network With Adversarial Defense
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3188583
Abstract: Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from the… read more here.
Keywords: using graph; malware; android malware; graph ... See more keywords
Investigate Financial Crime Patterns Using Graph Databases
Sign Up to like & getrecommendations! Published in 2022 at "IT Professional"
DOI: 10.1109/mitp.2022.3157029
Abstract: Law enforcement, legal authorities, financial fraud, and financial investigators seek evidence of financial crimes, and graph technologies provide a unique opportunity to uncover financial criminals by reviewing all-inclusive entities involved, their relationships to identify suspicious… read more here.
Keywords: financial crime; crime patterns; investigate financial; using graph ... See more keywords
Learning Structural Node Representations Using Graph Kernels
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2019.2947478
Abstract: Many applications require identifying nodes that perform similar functions in a graph. For instance, identifying structurally equivalent nodes can provide insight into the structure of complex networks. Learning latent representations that capture such structural role… read more here.
Keywords: node representations; structural node; learning structural; using graph ... See more keywords
Threatify: APT Threat Variant Generation Using Graph-Based Machine Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Network and Service Management"
DOI: 10.1109/tnsm.2025.3581463
Abstract: Ensuring cybersecurity in an ever-evolving threat landscape requires proactive identification and understanding of potential threats. Conventional detection and prediction solutions often fall short as they predominantly focus on known attack vectors. Advanced Persistent Threats (APTs)… read more here.
Keywords: attack; based machine; threat; graph based ... See more keywords