The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

通过计算预测化合物是否可治疗疾病的能力将提高经济性和药物批准的成功率。这项研究描述了Rephetio项目，以基于755种现有治疗方法来系统地模拟药物疗效。首先，我们构建了Hetionet（neo4j.het.io），这是一个集成网络，对来自数百万生物医学研究的知识进行编码。Hetionet v1.0包含11种类型的47,031个节点和24种类型的2,250,197个关系。来自29个公共资源的数据已整合在一起，以连接化合物，疾病，基因，解剖结构，途径，生物学过程，分子功能，细胞成分，药理学类别，副作用和症状。接下来，我们确定了区分治疗与非治疗的网络模式。然后我们预测了209,168种复合疾病对的治疗可能性（het。io / re用途）。我们的预测在两种外部治疗方法上均得到验证，并提供了有关癫痫的药理学见解，表明它们将有助于优先考虑药物替代用途。这项研究是完全开放的，并收到了40位社区成员的实时反馈。