MetaPred:A webserver for the Prediction of Cytochrome P450 Isoform responsible for Metabolizing a Drug Molecule

    Toxipred |KiDoQ | GDoQ | NPTOPE |KetoDrug |CRDD |OSDD |IMTECH | Raghava

** If you are using this server, please cite:: Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule BMC Pharmacology 2010, 10:8 **

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Inhibitor Prediction

  KiDoQ (Mtb target)

  GDoQ (Mtb target)

  ABMpred (Mtb target)

  eBooster (Mtb target)

  MDRIpred (Mtb cell)

  CancerIN (Cancer)

  ntEGFR (Cancer EGFR)

  EGFRpred (Cancer EGFR)

  DiPCell (Pancreatic Cancer)

  DMKPred (Human Kinases)

  TLR4HI (Human TLR4)

  HIVFin (HIV)

Antigenic Properties

ADMET Properties

  MetaPred (Cytochrome P450)

  ToxiPred (Aqueous toxicity)

  DrugMint (Drug-like)

  QED (Oral drug-like)

  Format Conversion



Cytochrome P450 enzymes (CYPs) are a multi gene family of heme-containing isoenzymes that are involved in oxidative metabolism of drug, steroids and carcinogens. About sixty CYPs are reported in human genome, but more than 90% of all therapeutic drugs are metabolized by five isoforms i.e. CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4.

MetaPred Server predict metabolizing CYP isoform of a drug molecule/substrate, based on SVM models developed using CDK descriptors.This server will be helpful for researcher working in the field of drug discovery.This study demonstrates that it is possible to develop free web servers in the field of chemoinformatics. This will encourage other researchers to develop web server for public use, which may lead to decrease the cost of discovering new drug molecules. In the following flow digaram we have given the example of CYP3A4, how this study will be helpful in drug design.

The purpose of this server is to predict the isoform which is responsible for the metabolism of given drug molecules. Therefore, five CYP isoform were used in this study along with only those molecules, which are specifically metabolized by particular CYP isoform. We developed all models on a clean and large dataset, created from the latest release of DrugBank. Present study we developed two types of model; I) single label models where model predict best single metabolizing isoform of a drug molecule and ii) muti label models where model predict number of metabolizing isoform of a drug.