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

Título traducido de la contribución: Proceso de estimación del descubrimiento de fármacos contra la tuberculosis mediante el uso de modelos de aprendizaje automático y profundo

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

Producción científica: Capítulo en Libro/Informe › Contribución a la conferencia

Resumen

La tuberculosis es una enfermedad contagiosa considerada de emergencia mundial por la Organización Mundial de la Salud. Uno de los problemas prevalentes más comunes está asociado a la tuberculosis farmacorresistente, debido al fracaso de los tratamientos con antibióticos. El uso de algoritmos de inteligencia artificial, principalmente modelos de aprendizaje automático (ML), ha permitido proporcionar más herramientas para el campo del descubrimiento de fármacos. Para este estudio, la metodología utilizada se orientó a identificar nuevos componentes que pudieran contribuir a la inhibición de la proteína inhA. Aprovechando los modelos ML que aprenden de los datos, se implementaron seis modelos de regresión. El mejor modelo obtuvo un valor R2 de 0,99 y un valor MSE de 1,8 e-5.

Título traducido de la contribución	Proceso de estimación del descubrimiento de fármacos contra la tuberculosis mediante el uso de modelos de aprendizaje automático y profundo
Idioma original	Inglés estadounidense
Título de la publicación alojada	Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers
Editores	Alvaro David Orjuela-Cañón, Jesus A Lopez, Julián David Arias-Londoño
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	43-53
Número de páginas	11
ISBN (versión impresa)	9783031484148
DOI	https://doi.org/10.1007/978-3-031-48415-5_4
Estado	Publicada - nov. 18 2023
Evento	6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia Duración: jul. 26 2023 → jul. 28 2023

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1865 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
País/Territorio	Colombia
Ciudad	Bogota
Período	7/26/23 → 7/28/23

Áreas temáticas de ASJC Scopus

Ciencia de la Computación General
Matemáticas General

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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

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Campos, M. S. R., Rodríguez, D. C., & Orjuela-Cañón, A. D. (2023). Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models. En 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, 2023. 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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note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 ; Conference date: 26-07-2023 Through 28-07-2023",

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Campos, MSR, Rodríguez, DC & Orjuela-Cañón, AD 2023, Tuberculosis Drug Discovery Estimation Process by Using Machine and Deep Learning Models. En 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, 2023. p. 43-53 (Communications in Computer and Information Science; Vol. 1865 CCIS).

Producción científica: Capítulo en Libro/Informe › Contribución a la conferencia

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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. En Orjuela-Cañón AD, Lopez JA, Arias-Londoño JD, editores, Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2023. 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

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