Due　to　rising　development　costs　and　stagnant　product　outputs　of　traditional　drug　discovery　methods,　drug　repositioning,　which　discovers　new　indications　for　existing　drugs,　has　attracted　increasing　interest.　Computational　drug　repositioning　can　integrate　prioritization　information　and　accelerate　time　lines　even　further.　However,　most　existing　methods　for　predicting　drug　repositioning　have　low　precisions.　The　present　article　proposed　a　new　method　named　DDAPRED　(https://github.com/nongdaxiaofeng/DDAPRED)　for　drug　repositioning　prediction.　The　method　integrated　multiple　sources　of　drug　similarity　and　disease　similarity　information,　and　it　used　the　regularized　logistic　matrix　decomposition　method　to　significantly　improve　the　prediction　performance.　In　5-fold　cross-validation,　the　areas　under　the　receiver　operating　characteristic　curve　(AUROC)　and　the　precision-recall　curve　(AUPRC)　of　DDAPRED　reached　0.932　and　0.438,　respectively,　exceeding　other　methods.　The　present　study　also　analyzed　the　parameters　influencing　the　model　performance　and　the　effect　of　different　drug　similarity　information　in-depth,　and　it　verified　the　treatment　relationship　of　the　top　50　predictions　with　unknown　relationships　in　the　training　set,　further　demonstrating　the　practicability　of　our　method.　©　2020,　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.