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. ...
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-...
In drug design, compound potency prediction is a popular machine learning application. Graph neural networks (GNNs) predict ligand affinity from graph representations of protein–ligand interactions typically extracted from X-ray structures. Despite some promising findings leading to claims that GNNs can ...
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...
Its main focus lies on giving an overview over the status-quo of what properties have been used in combination with GNNs. Otherwise this would be out of the scope of this short review. Graph neural networks This section provides a short introduction to graph neural-networks (GNN) and also ...
Molecular property prediction is an essential task in drug discovery. Recently, deep neural networks have accelerated the discovery of compounds with improved molecular profiles for effective drug development. In particular, graph neural networks (GNNs) have played a pivotal role in identifying promising...
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 ...
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...
Perhaps, one of the most promising results of using GNNs for drug discovery was from the researchers at MIT and their collaboratorspublished in Cell (2020). In this work, a deep GNN model, calledChemprop, is trained to predict whether a molecule exhibits the antibiotics properties: the growth...
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. ...