The accumulation of various types of drug informatics data and computational approaches for drug repositioning can accelerate pharmaceutical research and development. However, the integration of multi-dimensional drug data for precision repositioning remains a pressing challenge. Here, we propose a systematic framework named PIMD to predict drug therapeutic properties by integrating multi-dimensional data for drug repositioning. In PIMD, drug similarity networks (DSNs) based on chemical, pharmacological, and clinical data are fused into an integrated DSN (iDSN) composed of many clusters. Rather than simple fusion, PIMD offers a systematic way to annotate clusters. Unexpected drugs within clusters and drug pairs with a high iDSN similarity score are therefore identified to predict novel therapeutic uses. PIMD provides new insights into the universality, individuality, and complementarity of different drug properties by evaluating the contribution of each property data. To test the performance of PIMD, we use chemical, pharmacological, and clinical properties to generate an iDSN. Analyses of the contributions of each drug property indicate that this iDSN was driven by all data types and performs better than other DSNs. Within the top 20 recommended drug pairs, 7 drugs have been reported to be repurposed. The source code for PIMD is available at https://github.com/Sepstar/PIMD/.

各类药物信息学数据的积累和药物重新定位的计算方法可以加速药物研发。然而，整合多维药物数据以进行精确重新定位仍然是一个紧迫的挑战。在这里，我们提出了一个名为 PIMD 的系统框架，通过整合多维数据进行药物重新定位来预测药物治疗特性。在 PIMD 中，基于化学、药理学和临床数据的药物相似性网络 (DSN) 融合成由许多集群组成的集成 DSN (iDSN)。PIMD 不是简单的融合，而是提供了一种系统的方法来注释集群。因此，识别出具有高 iDSN 相似性评分的集群和药物对中的意外药物来预测新的治疗用途。PIMD 提供了对普遍性、个性化、通过评估每个属性数据的贡献，不同药物属性的互补性。为了测试 PIMD 的性能，我们使用化学、药理学和临床特性来生成 iDSN。对每种药物属性贡献的分析表明，该 iDSN 由所有数据类型驱动，并且性能优于其他 DSN。在推荐的前 20 对药物中，据报道有 7 种药物被重新利用。PIMD 的源代码可在 https://github.com/Sepstar/PIMD/ 获得。据报道，有 7 种药物被重新利用。PIMD 的源代码可在 https://github.com/Sepstar/PIMD/ 获得。据报道，有 7 种药物被重新利用。PIMD 的源代码可在 https://github.com/Sepstar/PIMD/ 获得。