Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models

Michael S.Ramirez Campos; Diana C. Rodríguez; Alvaro D. Orjuela-Cañón

doi:10.1007/978-3-031-48415-5_4

Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models

Michael S.Ramirez Campos, Diana C. Rodríguez, Alvaro D. Orjuela-Cañón

School of Medicine and Health Sciences

Research output: Chapter in Book/Report › Conference 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.

Original language	English (US)
Title of host publication	Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers
Editors	Alvaro David Orjuela-Cañón, Jesus A Lopez, Julián David Arias-Londoño
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	43-53
Number of pages	11
ISBN (Print)	9783031484148
DOIs	https://doi.org/10.1007/978-3-031-48415-5_4
State	Published - 2024
Event	6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia Duration: Jul 26 2023 → Jul 28 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1865 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
Country/Territory	Colombia
City	Bogota
Period	7/26/23 → 7/28/23

All Science Journal Classification (ASJC) codes

General Computer Science
General Mathematics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-48415-5_4

Cite this

Campos, M. S. R., Rodríguez, D. C., & Orjuela-Cañón, A. D. (2024). Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models. In A. D. Orjuela-Cañón, J. A. Lopez, & J. D. Arias-Londoño (Eds.), Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers (pp. 43-53). (Communications in Computer and Information Science; Vol. 1865 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-48415-5_4

Campos, Michael S.Ramirez ; Rodríguez, Diana C. ; Orjuela-Cañón, Alvaro D. / Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models. Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers. editor / Alvaro David Orjuela-Cañón ; Jesus A Lopez ; Julián David Arias-Londoño. Springer Science and Business Media Deutschland GmbH, 2024. pp. 43-53 (Communications in Computer and Information Science).

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title = "Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models",

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.",

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Campos, MSR, Rodríguez, DC & Orjuela-Cañón, AD 2024, Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models. in AD Orjuela-Cañón, JA Lopez & JD Arias-Londoño (eds), Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers. Communications in Computer and Information Science, vol. 1865 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 43-53, 6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023, Bogota, Colombia, 7/26/23. https://doi.org/10.1007/978-3-031-48415-5_4

Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models. / Campos, Michael S.Ramirez; Rodríguez, Diana C.; Orjuela-Cañón, Alvaro D.
Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers. ed. / Alvaro David Orjuela-Cañón; Jesus A Lopez; Julián David Arias-Londoño. Springer Science and Business Media Deutschland GmbH, 2024. p. 43-53 (Communications in Computer and Information Science; Vol. 1865 CCIS).

Research output: Chapter in Book/Report › Conference contribution

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T1 - Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models

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Y1 - 2024

N2 - 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.

AB - 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.

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Campos MSR, Rodríguez DC, Orjuela-Cañón AD. Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models. In Orjuela-Cañón AD, Lopez JA, Arias-Londoño JD, editors, Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2024. p. 43-53. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-48415-5_4

Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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