title: Drug Side Effect Profiles as Molecular Descriptors for Predictive Modeling of Target Bioactivity.
abstract: We have explored the potential of using side effect profiles of drugs to predict their bioactivities at the receptor level. Serotonin 5-HT6 binding and dopamine antagonism were investigated in separate studies. A set of 5-HT6 binders and non-binders was retrieved from the PDSP Ki database, whereas dopamine antagonists were retrieved from the MeSH Pharmaceutical Action file. The side effect data was extracted from ChemoText, a data repository containing MeSH annotations pulled from MEDLINE records. These side effects profiles were treated as molecular descriptors enabling a QSAR-like approach to build models that could reliably discriminate different classes of molecules, e.g., binders versus non-binders, and dopamine antagonists versus non-antagonists. Selected models with the best external prediction performances were applied to a library of ca. 1000 chemicals with known side effects profiles in order to predict their potential 5-HT6 binding and/or dopamine antagonism. In each case the virtual screening process was able to identify putatively active compounds that through subsequent literature-based validation were found to be likely or known 5-HT6 binders or dopamine antagonists. These results demonstrate that side effect profiles can be utilized to predict a drug's unknown molecular activity, thus representing a valuable opportunity in repositioning the drug for a new indications.
authors.affiliation: Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA phone: 919-966-2955; fax: 919-966-0204.
authors.forename: Nancy C
authors.initials: NC
authors.lastname: Baker
authors.affiliation: Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA phone: 919-966-2955; fax: 919-966-0204.
authors.forename: Denis
authors.initials: D
authors.lastname: Fourches
authors.affiliation: Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA phone: 919-966-2955; fax: 919-966-0204. alex_tropsha@unc.edu.
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
mesh_terms: D000818:Animals; D057225:Data Mining / Q000379:methods*; D018492:Dopamine Antagonists / Q000009:adverse effects*; D006801:Humans; D016239:MEDLINE; D008954:Models, Biological; D012702:Serotonin Antagonists / Q000009:adverse effects*
chemical_list: D018492:Dopamine Antagonists; D012702:Serotonin Antagonists
keywords: Drug repurposing; Machine learning; QSAR; Side effects