Abstract
As a key technology of rapid and low-cost drug development, drug repositioning is getting popular. In this study, a text mining approach to the discovery of unknown drug-disease relation was tested. Using a word embedding algorithm, senses of over 1.7 million words were well represented in sufficiently short feature vectors. Through various analysis including clustering and classification, feasibility of our approach was tested. Finally, our trained classification model achieved 87.6% accuracy in the prediction of drug-disease relation in cancer treatment and succeeded in discovering novel drug-disease relations that were actually reported in recent studies.
Highlights
To develop an effective and highly-demanded drug, hundreds million dollars and 10 or more years for R & D and clinical trial are typically required
One of the famous examples of drug repositioning is a treatment of multiple myeloma by thalidomide that was initially developed for relieving nausea and vomiting in pregnancy
One of the reasons why word embedding by word2vec becomes popular is its functionality of word analogy [6]
Summary
To develop an effective and highly-demanded drug, hundreds million dollars and 10 or more years for R & D and clinical trial are typically required. Structure-based drug design (SBDD) is actively studied to reduce the cost and time by in-silico screening of candidate chemicals [1] [2]; still it requires long time for tests on animals and human. Against such a background, a concept of drug repositioning (or drug repurposing, reprofiling, etc.) is attracting much interest and expectation from academic researchers and pharmaceutical companies [3].
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