ResGAT: Residual Graph Attention Networks for molecular property prediction

Molecular property prediction is an important step in the drug discovery pipeline. Numerous computational methods have been developed to predict a wide range of molecular properties. While recent approaches have shown promising results, no single architecture can comprehensively address all tasks, m...

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Bibliographic Details
Published inMemetic computing Vol. 16; no. 3; pp. 491 - 503
Main Authors Nguyen-Vo, Thanh-Hoang, Do, Trang T. T., Nguyen, Binh P.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024
Springer Nature B.V
Subjects
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Complex Systems
Control
Datasets
Deep learning
Engineering
Graph theory
Machine learning
Mathematical and Computational Engineering
Mechatronics
Molecular properties
Networks
Regular Research Paper
Robotics
Structured data
Graph convolutional network
Shortcut connection
Molecular property prediction
Residual
Graph attention network
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Summary:Molecular property prediction is an important step in the drug discovery pipeline. Numerous computational methods have been developed to predict a wide range of molecular properties. While recent approaches have shown promising results, no single architecture can comprehensively address all tasks, making this area persistently challenging and requiring substantial time and effort. Beyond traditional machine learning and deep learning architectures for regular data, several deep learning architectures have been designed for graph-structured data to overcome the limitations of conventional methods. Utilizing graph-structured data in quantitative structure–activity relationship (QSAR) modeling allows models to effectively extract unique features, especially where connectivity information is crucial. In our study, we developed residual graph attention networks (ResGAT), a deep learning architecture for molecular graph-structured data. This architecture is a combination of graph attention networks and shortcut connections to address both regression and classification problems. It is also customizable to adapt to various dataset sizes, enhancing the learning process based on molecular patterns. When tested multiple times with both random and scaffold sampling strategies on nine benchmark molecular datasets, QSAR models developed using ResGAT demonstrated stability and competitive performance compared to state-of-the-art methods.
ISSN:1865-9284
1865-9292
DOI:10.1007/s12293-024-00423-5