Drug-target networks have aided in many target prediction studies aiming at drug repurposing or the analysis of side effects. Conventional drug-target networks are bipartite. They contain two different types of nodes representing drugs and targets, respectively, and edges indicating pairwise drug-target interactions. In this work, we introduce a tripartite network consisting of drugs, other bioactive compounds, and targets from different sources. On the basis of analog relationships captured in the network and so-called neighbor targets of drugs, new drug targets can be inferred. The tripartite network was found to have a stable structure and simulated network growth was accompanied by a steady increase in assortativity, reflecting increasing correlation between degrees of connected nodes leading to even network connectivity. Local drug environments in the tripartite network typically contained neighbor targets and revealed interesting drug-compound-target relationships for further analysis. Candidate targets were prioritized. The tripartite network design extends standard drug-target networks and provides additional opportunities for drug target prediction.

中文翻译：

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设计用于预测药物靶标的三方网络。

药物靶标网络已帮助许多目标预测研究，旨在进行药物再利用或副作用分析。常规的药物靶向网络是两部分的。它们包含分别代表药物和靶标的两种不同类型的节点，以及指示成对药物-靶标相互作用的边缘。在这项工作中，我们介绍了由药物，其他生物活性化合物和不同来源的靶标组成的三方网络。根据网络中捕获的类比关系和所谓的毒品邻居目标，可以推断出新的毒品目标。发现该三方网络具有稳定的结构，并且模拟的网络增长伴随着分类能力的稳定提高，反映出所连接节点的程度之间的相关性不断提高，从而导致了网络的均匀连通性。三方网络中的本地药物环境通常包含相邻目标，并揭示了有趣的药物-化合物-目标关系，以进行进一步分析。优先考虑候选目标。三方网络设计扩展了标准的药物目标网络，并为预测药物目标提供了更多机会。