ResGAT: Residual Graph Attention Networks for molecular property prediction Article Swipe

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Thanh‐Hoang Nguyen‐Vo , T. T. Trang , Binh P. Nguyen ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1007/s12293-024-00423-5

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.

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Computer science Artificial intelligence Graph Machine learning Deep learning Quantitative structure–activity relationship Benchmark (surveying) Residual Pipeline (software) Property (philosophy) Data mining Theoretical computer science Algorithm Geodesy Programming language Philosophy Epistemology Geography

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Type: article
Language: en
Landing Page: https://doi.org/10.1007/s12293-024-00423-5
PDF: https://link.springer.com/content/pdf/10.1007/s12293-024-00423-5.pdf
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Cited By: 5
References: 26
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DOI: https://doi.org/10.1007/s12293-024-00423-5

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Title: ResGAT: Residual Graph Attention Networks for molecular property prediction

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Language: en

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Publication year: 2024

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Publication date: 2024-09-01

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Authors: Thanh‐Hoang Nguyen‐Vo, T. T. Trang, Binh P. Nguyen

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Landing page: https://doi.org/10.1007/s12293-024-00423-5

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Concepts: Computer science, Artificial intelligence, Graph, Machine learning, Deep learning, Quantitative structure–activity relationship, Benchmark (surveying), Residual, Pipeline (software), Property (philosophy), Data mining, Theoretical computer science, Algorithm, Geodesy, Programming language, Philosophy, Epistemology, Geography

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Cited by: 5

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