Molecular Compounds Proposal for Drug-Resistant Tuberculosis in the Drug Discovery Process

Research output: Chapter in Book/ReportConference contribution

Abstract

Tuberculosis is a contagious disease considered as world emergency by the World Health Organization. One of the common prevalent problems are associated to drug-resistant TB, because of unsuccessful treatments of using antibiotics. The use of artificial intelligence algorithms, mainly machine learning (ML) models have allowed to provided more tools for the drug discovery field. For this study, the methodology used was driven to identify new components that may contribute to the inhibition of the inhA protein. Leveraging ML models that learn from data, six regression models were implemented. Best model obtained R2 value of 0.99 and a MSE value of 1.8 e-5.

Translated title of the contributionPropuesta de compuestos moleculares para la tuberculosis farmacorresistente en el proceso de descubrimiento de fármacos
Original languageEnglish (US)
Title of host publication2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)
EditorsAlvaro David Orjuela-Canon
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9798350316599
DOIs
StatePublished - Aug 28 2023
Event2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia
Duration: Jul 26 2023Jul 28 2023

Publication series

Name2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Proceedings

Conference

Conference2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
Country/TerritoryColombia
CityBogota
Period7/26/237/28/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Control and Optimization

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