The impact of machine learning on future tuberculosis drug discovery

Research output: Contribution to journalEditorialOtherpeer-review

6 Citations (Scopus)

Abstract

Tuberculosis (TB) is an infectious disease, mainly infecting the lung, that is usually caused by the Mycobacterium tuberculosis bacteria. It is one of the leading infectious disease killers, claiming 1.5 million lives each year. Of the 10 million individuals who become ill with TB each year, ~30% are not identified by health systems. According to the US Centers for Disease Control in 2018, 1.7 billion people (23% of the world’s population) are infected with TB (https://www.cdc.gov/globalhealth/newsroom/topics/tb/index.html). Although effective drugs like rifamycin and isoniazid are available, resistance to anti-TB drugs due to drug misuse is a serious issue. There is a strong need to develop new, potent drugs to treat TB and prevent its spread.
Original languageEnglish
Pages (from-to)925-927
Number of pages3
JournalExpert Opinion on Drug Discovery
Volume17
Issue number9
DOIs
Publication statusPublished - 2 Sept 2022

Keywords

  • 3D-QSAR
  • Bayesian
  • CoMFA
  • COMSIA
  • drug discovery
  • machine learning
  • molecular docking
  • Mycobacterium tuberculosis
  • random forest
  • support vector machines
  • virtual screening

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