| Home | Submit | Dataset | References | Team | Contact | CRDD |

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



=== If you are using this server please cite: === Garg, A., Tewari, R. and Raghava, G. P. S. (2010) KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials. BMC Bioinformatics 2010, 11:125 === **** KiDoQ: A server for predicting drug/inhibitor against bacte\ ria using docking and QSAR ****

KiDoQ, a web server has been developed to serve scientific community working in the field of designing inhibitors against Dihydrodipicolinate synthase (DHDPS), a potential drug target enzyme of a unique bacterial DAP/Lysine pathway. The server has employed the molecular docking and ligand based QSAR strategies to predict inhibitory activity value (Ki) of small compounds for DHDPS enzyme. The algorithm behind the server includes the docking of compounds to the active binding site of enzyme followed by QSAR modeling where, docking generated energy based scoring values (for the best conformer) are cascaded as input variables to QSAR based SVM model for prediction of Ki value. The QSAR model implemented on the server has been trained on the dataset of 23 inhbitors of DHDPS and predict the Ki value with correlation R/q2 values of 0.93/0.80 and MAE of 1.89.

Institute of Microbial Technology