Drug repositioning strategies have improved substantially in recent years. At 
present, two advances are poised to facilitate new strategies. First, the LINCS 
project can provide rich transcriptome data that reflect the responses of cells 
upon exposure to various drugs. Second, machine learning algorithms have been 
applied successfully in biomedical research. In this paper, we developed a 
systematic method to discover novel indications for existing drugs by 
approaching drug repositioning as a multi-label classification task and used a 
Softmax regression model to predict previously unrecognized therapeutic 
properties of drugs based on LINCS transcriptome data. This approach to complete 
the said task has not been achieved in previous studies. By performing in silico 
comparison, we demonstrated that the proposed Softmax method showed markedly 
superior performance over those of other methods. Once fully trained, the method 
showed a training accuracy exceeding 80% and a validation accuracy of 
approximately 70%. We generated a highly credible set of 98 drugs with high 
potential to be repositioned for novel therapeutic purposes. Our case studies 
included zonisamide and brinzolamide, which were originally developed to treat 
indications of the nervous system and sensory organs, respectively. Both drugs 
were repurposed to the cardiovascular category.