Drug discovery in healthcare. To develop new candidate medications quickly, researchers need to compute the physical properties of molecules. GNNs provide an efficient way to represent these complex 3D structure
We build upon the GNN models with ideas from recent breakthroughs in geometric deep learning, inspired by the topologies of the molecules. In this poster paper, we present an overview of the drug discovery framework, drug-target interaction framework, and GNNs. Preliminary results on two COVID-...
Graphs are flexible mathematical objects that can represent many entities and knowledge from different domains, including in the life sciences. Graph neural networks (GNNs) are mathematical models that can learn functions over graphs and are a leading approach for building predictive models on graph-st...
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing ...
This study introduces a novel approach using Graph Neural Networks (GNNs) to integrate aquatic toxicity data, providing an alternative to complement traditional in vivo ecotoxicity testing. This study pioneers the application of GNN in ecotoxicology by formulating the problem as a relation prediction ...
Fraud Detection:Identifying suspicious activity in financial networks. Drug Discovery:Predicting molecular properties for new drug candidates. Challenges in GNN Despite their success, GNNs face several challenges, such as − Scalability:Training large graphs is computationally expensive. ...
While graph neural networks (GNNs) have been applied to DTA, existing GNNs have limitations in effectively extracting substructural features across various sizes. Functional groups play a crucial role in modulating molecular properties, but existing GNNs struggle with feature extraction from certain motifs...
2.3 SpreadGNN: Serverless Federated MTL for GNNs 为了实现无服务器环境下的图联邦多任务学习,本文引入了一种新的优化方法,称为分散的周期平均SGD(DPA-SGD)。DPA-SGD 的主要思想是,每个客户端在本地应用 SGD,并在每轮迭代的通信过程中仅与其邻居同步所有参数。在一个分散的系统中,所有的客户端不一定与其他所有...
in the development of GNNs20,21. Graph neural networks can be interpreted as the generalization of convolutional neural networks to irregular-shaped graph structures. While other machine learning methods, e.g., convolutional neural networks are at the peak of publication activity, GNNs are still ...
Drug discovery in healthcare. To develop new candidate medications quickly, researchers need to compute the physical properties of molecules. GNNs provide an efficient way to represent these complex 3D structures and accurately predict their properties. ...