GeNNius: an ultrafast drug-target interaction inference method based on graph neural networks.
GeNNius: an ultrafast drug-target interaction inference method based on graph neural networks.
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DOI：
10.1093/bioinformatics/btad774
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发表时间：
2024-01-02
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药物-靶标相互作用（DTI）预测是药物再利用领域中一个相关但具有挑战性的课题。硅片方法已经引起了特别的关注，因为它们可以减少传统方法的相关成本和时间承诺。然而，目前最先进的方法存在一些局限性：现有的DTI预测方法计算成本高，因此阻碍了使用大型网络和利用可用数据集的能力，并且DTI预测方法对未见数据集的推广仍然未被探索，这可能会在准确性和鲁棒性方面改善DTI推断方法的开发过程。在这项工作中，我们介绍了GeNNius（图嵌入神经网络交互揭示系统），这是一种基于图神经网络（GNN）的方法，在各种数据集的准确性和时间效率方面优于最先进的模型。我们还展示了其预测能力，通过评估每个数据集以前不知道的dti来发现新的相互作用。我们通过在不同的数据集上训练和测试来进一步评估GeNNius的泛化能力，表明该框架可以通过在大数据集上训练和在小数据集上测试来潜在地改善DTI预测任务。最后，我们定性地研究了GeNNius生成的嵌入，揭示了GNN编码器在图卷积后保持生物信息，同时通过节点扩散这些信息，最终在节点嵌入空间中区分蛋白质家族。GeNNius代码可从https://github.com/ubioinformat/GeNNius获得。
Drug–target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could potentially improve the development processes of DTI inferring approaches in terms of accuracy and robustness. In this work, we introduce GeNNius (Graph Embedding Neural Network Interaction Uncovering System), a Graph Neural Network (GNN)-based method that outperforms state-of-the-art models in terms of both accuracy and time efficiency across a variety of datasets. We also demonstrated its prediction power to uncover new interactions by evaluating not previously known DTIs for each dataset. We further assessed the generalization capability of GeNNius by training and testing it on different datasets, showing that this framework can potentially improve the DTI prediction task by training on large datasets and testing on smaller ones. Finally, we investigated qualitatively the embeddings generated by GeNNius, revealing that the GNN encoder maintains biological information after the graph convolutions while diffusing this information through nodes, eventually distinguishing protein families in the node embedding space. GeNNius code is available at https://github.com/ubioinformat/GeNNius.
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64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
3
作者：
通讯作者：
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影响因子：
5.6
作者：
Karki N;Verma N;Trozzi F;Tao P;Kraka E;Zoltowski B
通讯作者：
Zoltowski B
DOI：
10.1038/nrd.2016.230
发表时间：
2017-01
期刊：
Nature reviews. Drug discovery
影响因子：
--
作者：
Santos R;Ursu O;Gaulton A;Bento AP;Donadi RS;Bologa CG;Karlsson A;Al-Lazikani B;Hersey A;Oprea TI;Overington JP
通讯作者：
Overington JP
影响因子：
14.9
作者：
Gaulton A;Hersey A;Nowotka M;Bento AP;Chambers J;Mendez D;Mutowo P;Atkinson F;Bellis LJ;Cibrián-Uhalte E;Davies M;Dedman N;Karlsson A;Magariños MP;Overington JP;Papadatos G;Smit I;Leach AR
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Leach AR