AVCpred, a web based algorithm for prediction and design of antiviral compounds.

  • Antiviral compounds (AVCs) are a category of antimicrobial drugs used specially for treating viral infections by inhibiting the development of the viral pathogen inside the host cell.
  • In AVCpred we used Quantitative structure-activity relationships (QSAR) approach in which relationships connecting molecular descriptors and inhibition are used to predict the antiviral potential of chemical compounds.
  • In this method, we used previously known antiviral compounds against Human immunodeficiency virus (HIV), Hepatitis C virus (HCV), Hepatitis B virus (HBV), Human herpesvirus (HHV) and 26 other important viruses with experimentally validated percentage inhibition from ChEMBL, a large-scale bioactivity database for drug discovery.
  • This was followed by descriptor calculation and selection of best performing molecular descriptors. The latter were then used as input for Support Vector Machine (in regression mode) to develop QSAR models for different viruses as well as a general model for other viruses.
  • The server provides user friendly prediction/design options and also other tools for analysis.In addition, complete datasets of the inhibitors have also been included.
  • This server is hoped to be useful for researchers working on antiviral therapeutics development and also in identifying the best inhibitory compounds for further research.

     Citation: Qureshi, A., Kaur, G. and Kumar, M. (2016), AVCpred: An integrated web server for prediction and design of antiviral compounds. Chem Biol Drug Des. doi:10.1111/cbdd.12834. PMID: 27490990