Tieu Long Phan

Tieu Long Phan

MS. in Pharmacy; Expertise in applying machine learning techniques, cheminformatics and automated pipeline to screen new biologically active molecules.

In silico modeling for prediction of potential HIV integrase inhibitors

Published:

Abstract

HIV Integrase Strand Transfer Inhibitors (INSTIs) had been a trend in HIV treatment research in recent years. Many studies had shown that inhibiting the strand transfer of integrase would prevent the spread of the virus, prolonging the survival time for the host. The application of in silico models would help shorten the time, effort and resources to research and develop new potential HIV integrase inhibitors in HIV treatment.

Workflow

The project contains four parts:

  • Pharmacophore modeling: automated results extraction and comparasion; ensemble learning to enhance performance
  • QSAR (Classification and regression tasks): develop full data pipeline and statistical test
  • Molecular docking: using five docking softwares to perform retrospective control

thesis slide

Requirements

This module requires the following modules:

Note

Updating…

Folder segmentation

Usage

Contributing

Please visit the Thesis repository. Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.