GNN-DDAS:基于图神经网络的抗肉苁蓉小分子药物发现。

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Xin Zeng, Peng-Kun Feng, Shu-Juan Li, Shuang-Qing Lv, Meng-Liang Wen, Yi Li
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引用次数: 0

摘要

血吸虫病是一种热带疾病,对数亿人构成重大威胁,但却常常不为人们所注意。虽然吡喹酮是一种广泛使用的抗血吸虫药物,成本低、治愈率高,但它也有一些缺点。这些缺点包括对血吸虫幼虫无效、对幼儿的疗效降低以及新出现的耐药性。因此,发现新的活性抗血吸虫小分子至关重要,但这一过程面临着计算机辅助方法准确性低的挑战。为解决这一问题,我们提出了基于图神经网络(GNN)的新型深度学习框架 GNN-DDAS,该框架专为药物发现而设计,用于识别活性抗染色体(DDAS)小分子。最初,我们使用多层感知器从小分子 SMILES 的各种表征中提取序列特征。接着,使用 GNN 从分子图中提取结构特征。最后,将提取的序列和结构特征合并并输入全连接网络,以预测活性抗肉苁蓉小分子。实验结果表明,与基准方法相比,GNN-DDAS 在基准数据集和实际应用数据集上都表现出更优越的性能。此外,通过使用 GNNExplainer 模型,我们分析了小分子的关键亚结构特征,从而深入了解了 GNN-DDAS 的有效性。总之,GNN-DDAS 为发现新的活性抗肉苁蓉小分子提供了一种很有前景的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules.

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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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