title: Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions.
abstract: OBJECTIVE
  Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles.
  
  
  METHODS
  Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1.
  
  
  RESULTS
  QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71-100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds.
  
  
  CONCLUSIONS
  The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles.
authors.affiliation: Laboratory for Molecular Modeling Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina 100K Beard Hall, Chapel Hill, North Carolina, 27599-7568, USA.
authors.forename: Alexander
authors.initials: A
authors.lastname: Sedykh
authors.forename: Denis
authors.initials: D
authors.lastname: Fourches
authors.forename: Jianmin
authors.initials: J
authors.lastname: Duan
authors.forename: Oliver
authors.initials: O
authors.lastname: Hucke
authors.forename: Michel
authors.initials: M
authors.lastname: Garneau
authors.forename: Hao
authors.initials: H
authors.lastname: Zhu
authors.forename: Pierre
authors.initials: P
authors.lastname: Bonneau
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
mesh_terms: D001185:Artificial Intelligence; D001693:Biological Transport, Active; D003198:Computer Simulation; D062313:Databases, Pharmaceutical; D030562:Databases, Protein; D006801:Humans; D007413:Intestinal Mucosa / Q000378:metabolism*; D026901:Membrane Transport Proteins / Q000737:chemistry / Q000378:metabolism*; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000737:chemistry / Q000378:metabolism*; D010599:Pharmacokinetics; D021281:Quantitative Structure-Activity Relationship
chemical_list: D026901:Membrane Transport Proteins; D004364:Pharmaceutical Preparations