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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
Claim

MvGraphDTA: Multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs

Research datakeyboard_double_arrow_right Dataset 12 Aug 2024Publisher:Zenodo

doi: 10.5281/zenodo.13299715

MvGraphDTA: Multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs

Abstract

MvGraphDTA MvGraphDTA:通过引入图形和折线图,基于多视图的图形深度模型用于药物-靶点亲和力预测 要求 numpy==1.23.5 pandas==1.5.2 biopython==1.79 scipy==1.9.3 torch==2.0.1 torch_geometric==2.3.1 示例用法 1. 使用我们的预训练模型 在本节中,我们提供了 pdbbindv2016 的核心集数据和 Li 的数据(过滤后的 casf2013 和 casf2016),您可以直接执行以下命令来运行我们的预训练模型并在核心集上获取结果。 # Run the following command.python test_pretrain.py 2. 在数据集上运行 在本节中,您必须提供药物的 .sdf 文件以及靶标的 .pdb 文件。 # 您可以通过运行以下命令获取药物和靶点的图形和折线图。Python data_process.py # 当所有数据都准备好后,您可以通过运行以下命令来训练自己的模型。Python training.py

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average