DrugSig: drug induced gene signature for drug repositioning

Over the past decades, to bring a new drug to the market often takes billions of investment dollars and an average about 9-12 years. De novo drug discovery has grown to be time-consuming and costly. In light of these challenges, drug repositioning, which concerns the detection and development of new clinical indications for those existing drugs has emerged as an increasingly important strategy for the new drug discovery.
Historically, the discovery of new uses of old drugs is mostly through serendipity or resulted from a better understanding of the drugs’ mechanism of action. The efficacy of these methods is very low. With drug-related data growth and open data initiatives, a set of new repositioning strategies and techniques has emerged with integrating data from various sources, like pharmacological, genetic, chemical or clinical data. These methods can accumulate evidence supporting discovery of new uses or indications of existing drugs. However, currently existing strategies strongly rely on separated or individual experimental data, and resulted in inefficient outputs. The construction of an integrating drug-related database is a must.
We collected the gene expression data scattered in the separated database to develop the drug-related database. Further, we plan to construct the drug-drug interaction network based on this database by machine learning methods. The constructed network can server as a tool to quicken drug repositioning.
People involved:

Dr. Qingshan Huang
Dr. Hongyu Wu
Dr. Jinjiang Huang

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