Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371.
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China.
J Chem Inf Model. 2023 May 22;63(10):2928-2935. doi: 10.1021/acs.jcim.3c00045. Epub 2023 May 11.
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.
人工智能（AI）技术具有从根本上改变抗生素发现产业的巨大潜力。高效、有效的分子特征化是所有高度准确的抗生素发现学习模型的关键。在本文中，我们通过结合基于序列的 2D 指纹和基于结构的图表示，提出了一种指纹增强图注意网络（FinGAT）模型。在我们的特征学习过程中，序列信息被转换为指纹向量，结构信息通过 GAT 模块编码为另一个向量。这两个向量被连接起来并输入到多层感知机（MLP）中进行抗生素活性分类。我们的模型经过了广泛的测试，并与现有模型进行了比较。结果表明，我们的 FinGAT 在抗生素发现方面优于各种最先进的 GNN 模型。
