A simple and efficient graph Transformer architecture for molecular properties prediction
Graph Transformer architecture started to catch fire in molecular properties prediction due to its ability to represent complex interactions between all nodes. However, the self-attention mechanism in Transformer encoder part transforms the graph data into a fully-connected graph for graph represent...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lu, Yunhua [verfasserIn] Zeng, Kangli [verfasserIn] Zhang, Qingwei [verfasserIn] Zhang, Jun'an [verfasserIn] Cai, Lin [verfasserIn] Tian, Jiangling [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Chemical engineering science - Amsterdam [u.a.] : Elsevier Science, 1951, 280 |
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| Übergeordnetes Werk: |
volume:280 |
| DOI / URN: |
10.1016/j.ces.2023.119057 |
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| Katalog-ID: |
ELV061837679 |
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| 245 | 1 | 0 | |a A simple and efficient graph Transformer architecture for molecular properties prediction |
| 264 | 1 | |c 2023 | |
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| 520 | |a Graph Transformer architecture started to catch fire in molecular properties prediction due to its ability to represent complex interactions between all nodes. However, the self-attention mechanism in Transformer encoder part transforms the graph data into a fully-connected graph for graph representation learning, causing the loss of the original structural information of the graph. In this work, Local Transformer is proposed to keep the original graph structure and aggregate local information of nodes. In the model, a simple graph convolution is designed to replace the self-attention module, which reaches the state-of-the-art performance on ZINC dataset. Further, in order to rectify the problem that graph neural networks(GNNs) cannot capture long-range interactions of atoms, a novel end-to-end framework combined the GNN with Local and Global Transformer is proposed, which achieves good results on QM9 dataset. | ||
| 650 | 4 | |a Graph neural network | |
| 650 | 4 | |a Graph Transformer | |
| 650 | 4 | |a Graph representation | |
| 650 | 4 | |a Molecular properties prediction | |
| 700 | 1 | |a Zeng, Kangli |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Qingwei |e verfasserin |0 (orcid)0000-0003-3864-7769 |4 aut | |
| 700 | 1 | |a Zhang, Jun'an |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Jiangling |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Chemical engineering science |d Amsterdam [u.a.] : Elsevier Science, 1951 |g 280 |h Online-Ressource |w (DE-627)306717794 |w (DE-600)1501538-5 |w (DE-576)094503982 |7 nnns |
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10.1016/j.ces.2023.119057 doi (DE-627)ELV061837679 (ELSEVIER)S0009-2509(23)00613-9 DE-627 ger DE-627 rda eng 660 VZ 58.14 bkl Lu, Yunhua verfasserin aut A simple and efficient graph Transformer architecture for molecular properties prediction 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Graph Transformer architecture started to catch fire in molecular properties prediction due to its ability to represent complex interactions between all nodes. However, the self-attention mechanism in Transformer encoder part transforms the graph data into a fully-connected graph for graph representation learning, causing the loss of the original structural information of the graph. In this work, Local Transformer is proposed to keep the original graph structure and aggregate local information of nodes. In the model, a simple graph convolution is designed to replace the self-attention module, which reaches the state-of-the-art performance on ZINC dataset. Further, in order to rectify the problem that graph neural networks(GNNs) cannot capture long-range interactions of atoms, a novel end-to-end framework combined the GNN with Local and Global Transformer is proposed, which achieves good results on QM9 dataset. Graph neural network Graph Transformer Graph representation Molecular properties prediction Zeng, Kangli verfasserin aut Zhang, Qingwei verfasserin (orcid)0000-0003-3864-7769 aut Zhang, Jun'an verfasserin aut Cai, Lin verfasserin aut Tian, Jiangling verfasserin aut Enthalten in Chemical engineering science Amsterdam [u.a.] : Elsevier Science, 1951 280 Online-Ressource (DE-627)306717794 (DE-600)1501538-5 (DE-576)094503982 nnns volume:280 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.14 Chemische Reaktionstechnik VZ AR 280 |
| spelling |
10.1016/j.ces.2023.119057 doi (DE-627)ELV061837679 (ELSEVIER)S0009-2509(23)00613-9 DE-627 ger DE-627 rda eng 660 VZ 58.14 bkl Lu, Yunhua verfasserin aut A simple and efficient graph Transformer architecture for molecular properties prediction 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Graph Transformer architecture started to catch fire in molecular properties prediction due to its ability to represent complex interactions between all nodes. However, the self-attention mechanism in Transformer encoder part transforms the graph data into a fully-connected graph for graph representation learning, causing the loss of the original structural information of the graph. In this work, Local Transformer is proposed to keep the original graph structure and aggregate local information of nodes. In the model, a simple graph convolution is designed to replace the self-attention module, which reaches the state-of-the-art performance on ZINC dataset. Further, in order to rectify the problem that graph neural networks(GNNs) cannot capture long-range interactions of atoms, a novel end-to-end framework combined the GNN with Local and Global Transformer is proposed, which achieves good results on QM9 dataset. Graph neural network Graph Transformer Graph representation Molecular properties prediction Zeng, Kangli verfasserin aut Zhang, Qingwei verfasserin (orcid)0000-0003-3864-7769 aut Zhang, Jun'an verfasserin aut Cai, Lin verfasserin aut Tian, Jiangling verfasserin aut Enthalten in Chemical engineering science Amsterdam [u.a.] : Elsevier Science, 1951 280 Online-Ressource (DE-627)306717794 (DE-600)1501538-5 (DE-576)094503982 nnns volume:280 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.14 Chemische Reaktionstechnik VZ AR 280 |
| allfields_unstemmed |
10.1016/j.ces.2023.119057 doi (DE-627)ELV061837679 (ELSEVIER)S0009-2509(23)00613-9 DE-627 ger DE-627 rda eng 660 VZ 58.14 bkl Lu, Yunhua verfasserin aut A simple and efficient graph Transformer architecture for molecular properties prediction 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Graph Transformer architecture started to catch fire in molecular properties prediction due to its ability to represent complex interactions between all nodes. However, the self-attention mechanism in Transformer encoder part transforms the graph data into a fully-connected graph for graph representation learning, causing the loss of the original structural information of the graph. In this work, Local Transformer is proposed to keep the original graph structure and aggregate local information of nodes. In the model, a simple graph convolution is designed to replace the self-attention module, which reaches the state-of-the-art performance on ZINC dataset. Further, in order to rectify the problem that graph neural networks(GNNs) cannot capture long-range interactions of atoms, a novel end-to-end framework combined the GNN with Local and Global Transformer is proposed, which achieves good results on QM9 dataset. Graph neural network Graph Transformer Graph representation Molecular properties prediction Zeng, Kangli verfasserin aut Zhang, Qingwei verfasserin (orcid)0000-0003-3864-7769 aut Zhang, Jun'an verfasserin aut Cai, Lin verfasserin aut Tian, Jiangling verfasserin aut Enthalten in Chemical engineering science Amsterdam [u.a.] : Elsevier Science, 1951 280 Online-Ressource (DE-627)306717794 (DE-600)1501538-5 (DE-576)094503982 nnns volume:280 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.14 Chemische Reaktionstechnik VZ AR 280 |
| allfieldsGer |
10.1016/j.ces.2023.119057 doi (DE-627)ELV061837679 (ELSEVIER)S0009-2509(23)00613-9 DE-627 ger DE-627 rda eng 660 VZ 58.14 bkl Lu, Yunhua verfasserin aut A simple and efficient graph Transformer architecture for molecular properties prediction 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Graph Transformer architecture started to catch fire in molecular properties prediction due to its ability to represent complex interactions between all nodes. However, the self-attention mechanism in Transformer encoder part transforms the graph data into a fully-connected graph for graph representation learning, causing the loss of the original structural information of the graph. In this work, Local Transformer is proposed to keep the original graph structure and aggregate local information of nodes. In the model, a simple graph convolution is designed to replace the self-attention module, which reaches the state-of-the-art performance on ZINC dataset. Further, in order to rectify the problem that graph neural networks(GNNs) cannot capture long-range interactions of atoms, a novel end-to-end framework combined the GNN with Local and Global Transformer is proposed, which achieves good results on QM9 dataset. Graph neural network Graph Transformer Graph representation Molecular properties prediction Zeng, Kangli verfasserin aut Zhang, Qingwei verfasserin (orcid)0000-0003-3864-7769 aut Zhang, Jun'an verfasserin aut Cai, Lin verfasserin aut Tian, Jiangling verfasserin aut Enthalten in Chemical engineering science Amsterdam [u.a.] : Elsevier Science, 1951 280 Online-Ressource (DE-627)306717794 (DE-600)1501538-5 (DE-576)094503982 nnns volume:280 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.14 Chemische Reaktionstechnik VZ AR 280 |
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A simple and efficient graph Transformer architecture for molecular properties prediction |
| author_sort |
Lu, Yunhua |
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Chemical engineering science |
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Chemical engineering science |
| lang_code |
eng |
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| dewey-hundreds |
600 - Technology |
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marc |
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2023 |
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Lu, Yunhua Zeng, Kangli Zhang, Qingwei Zhang, Jun'an Cai, Lin Tian, Jiangling |
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Elektronische Aufsätze |
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Lu, Yunhua |
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10.1016/j.ces.2023.119057 |
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(ORCID)0000-0003-3864-7769 |
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(orcid)0000-0003-3864-7769 |
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verfasserin |
| title_sort |
a simple and efficient graph transformer architecture for molecular properties prediction |
| title_auth |
A simple and efficient graph Transformer architecture for molecular properties prediction |
| abstract |
Graph Transformer architecture started to catch fire in molecular properties prediction due to its ability to represent complex interactions between all nodes. However, the self-attention mechanism in Transformer encoder part transforms the graph data into a fully-connected graph for graph representation learning, causing the loss of the original structural information of the graph. In this work, Local Transformer is proposed to keep the original graph structure and aggregate local information of nodes. In the model, a simple graph convolution is designed to replace the self-attention module, which reaches the state-of-the-art performance on ZINC dataset. Further, in order to rectify the problem that graph neural networks(GNNs) cannot capture long-range interactions of atoms, a novel end-to-end framework combined the GNN with Local and Global Transformer is proposed, which achieves good results on QM9 dataset. |
| abstractGer |
Graph Transformer architecture started to catch fire in molecular properties prediction due to its ability to represent complex interactions between all nodes. However, the self-attention mechanism in Transformer encoder part transforms the graph data into a fully-connected graph for graph representation learning, causing the loss of the original structural information of the graph. In this work, Local Transformer is proposed to keep the original graph structure and aggregate local information of nodes. In the model, a simple graph convolution is designed to replace the self-attention module, which reaches the state-of-the-art performance on ZINC dataset. Further, in order to rectify the problem that graph neural networks(GNNs) cannot capture long-range interactions of atoms, a novel end-to-end framework combined the GNN with Local and Global Transformer is proposed, which achieves good results on QM9 dataset. |
| abstract_unstemmed |
Graph Transformer architecture started to catch fire in molecular properties prediction due to its ability to represent complex interactions between all nodes. However, the self-attention mechanism in Transformer encoder part transforms the graph data into a fully-connected graph for graph representation learning, causing the loss of the original structural information of the graph. In this work, Local Transformer is proposed to keep the original graph structure and aggregate local information of nodes. In the model, a simple graph convolution is designed to replace the self-attention module, which reaches the state-of-the-art performance on ZINC dataset. Further, in order to rectify the problem that graph neural networks(GNNs) cannot capture long-range interactions of atoms, a novel end-to-end framework combined the GNN with Local and Global Transformer is proposed, which achieves good results on QM9 dataset. |
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| title_short |
A simple and efficient graph Transformer architecture for molecular properties prediction |
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| author2 |
Zeng, Kangli Zhang, Qingwei Zhang, Jun'an Cai, Lin Tian, Jiangling |
| author2Str |
Zeng, Kangli Zhang, Qingwei Zhang, Jun'an Cai, Lin Tian, Jiangling |
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10.1016/j.ces.2023.119057 |
| up_date |
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