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Part 1: Document Description
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Citation |
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Title: |
Replication Data for: Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery |
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Identification Number: |
doi:10.21979/N9/FGKV7S |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2023-06-23 |
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Version: |
1 |
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Bibliographic Citation: |
Choo, Hou Yee; Wee, JunJie; Shen, Cong; Xia, Kelin, 2023, "Replication Data for: Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery", https://doi.org/10.21979/N9/FGKV7S, DR-NTU (Data), V1 |
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Citation |
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Title: |
Replication Data for: Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery |
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Identification Number: |
doi:10.21979/N9/FGKV7S |
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Authoring Entity: |
Choo, Hou Yee (Nanyang Technological University) |
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Wee, JunJie (Nanyang Technological University) |
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Shen, Cong (Hunan University) |
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Xia, Kelin (Nanyang Technological University) |
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Software used in Production: |
Python |
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Grant Number: |
M4081842.110 |
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Grant Number: |
RG109/19 |
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Grant Number: |
MOE-T2EP20120-0013 |
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Grant Number: |
MOE-T2EP20220-0010 |
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Grant Number: |
MOE-T2EP20221-0003 |
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Distributor: |
DR-NTU (Data) |
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Access Authority: |
Wee, JunJie |
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Depositor: |
Wee, JunJie |
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Date of Deposit: |
2023-06-23 |
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Holdings Information: |
https://doi.org/10.21979/N9/FGKV7S |
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Study Scope |
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Keywords: |
Mathematical Sciences, Medicine, Health and Life Sciences, Mathematical Sciences, Medicine, Health and Life Sciences, Antimicrobial agents, Bacteria, Layers, Molecular Modeling, Molecules |
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Abstract: |
Artificial Intelligence (AI) techniques are of great potential to fundamentally change antibiotic discovery industries. Efficient and effective molecular featurization is key to all highly accurate learning models for antibiotic discovery. In this paper, we propose a fingerprint-enhanced graph attention network (FinGAT) model by the combination of sequence-based 2D fingerprints and structure-based graph representation. In our feature learning process, sequence information is transformed into a fingerprint vector, and structural information is encoded through a GAT module into another vector. These two vectors are concatenated and input into a multilayer perceptron (MLP) for antibiotic activity classification. Our model is extensively tested and compared with existing models. It has been found that our FinGAT can outperform various state-of-the-art GNN models in antibiotic discovery. |
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Kind of Data: |
Calculated data and codes |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.1021/acs.jcim.3c00045 |
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Bibliographic Citation: |
Choo, H. Y., Wee, J., Shen, C., & Xia, K. (2023). Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery. Journal of Chemical Information and Modeling, 63 (10), 2928-2935. |
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Label: |
FinGAT.rar |
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Notes: |
application/x-rar-compressed |