MOTIVATION: Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs. RESULTS: We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing. AVAILABILITY AND IMPLEMENTATION: The R scripts are available on demand from the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs. RESULTS: We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing. AVAILABILITY AND IMPLEMENTATION: The R scripts are available on demand from the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Michael J Keiser; Vincent Setola; John J Irwin; Christian Laggner; Atheir I Abbas; Sandra J Hufeisen; Niels H Jensen; Michael B Kuijer; Roberto C Matos; Thuy B Tran; Ryan Whaley; Richard A Glennon; Jérôme Hert; Kelan L H Thomas; Douglas D Edwards; Brian K Shoichet; Bryan L Roth Journal: Nature Date: 2009-11-01 Impact factor: 49.962
Authors: Michael J Keiser; Bryan L Roth; Blaine N Armbruster; Paul Ernsberger; John J Irwin; Brian K Shoichet Journal: Nat Biotechnol Date: 2007-02 Impact factor: 54.908