Oct 19, 2022 · In molecular activity prediction, each atom of a molecular is considered as a node of a graph, and a bond is regarded as an edge of a graph.

In this article we propose a graph structure learning (GSL) based MPP approach, called GSL-MPP. Specifically, we first apply graph neural network (GNN) over ...

Molecular machine learning based on graph neural network has a broad prospect in molecular property identification in drug discovery.

Introduction · Materials and Methods · References

Sep 30, 2023 · DGDTA, which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this ...

Apr 5, 2024 · Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations ...

We propose the ATT-MLP model that combines attention mechanism and multi-layer perception(MLP), and applys node attention weights to graph pooling. Experiments ...

Mar 26, 2024 · We present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway–pathway ...

In this study, we developed an attention-based graph neural network, PredPS to predict the plasma stability of compounds in human plasma using in-house and ...

Sep 3, 2024 · In our study, we developed residual graph attention networks (ResGAT), a deep learning architecture for molecular graph-structured data.