Fingerprint-enhanced graph attention network (FinGAT) model for antibiotic discovery

Fingerprint-enhanced graph attention network (FinGAT) model for antibiotic discovery

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 atten...

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Bibliographic Details
Main Authors: Choo, Hou Yee, Wee, Junjie, Shen, Cong, Xia, Kelin
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2023
Subjects:
Science::Mathematics
Anti-bacterial Agents
Artificial Intelligence
Online Access:https://hdl.handle.net/10356/170327
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Institution: Nanyang Technological University
Language: English
Description
Summary: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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