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

Michael Ramirez Campos; Diana C. Rodriguez; Alvaro D. Orjuela-Canon

doi:10.1109/colcaci59285.2023.10225875

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

Título traducido de la contribución: Propuesta de compuestos moleculares para la tuberculosis farmacorresistente en el proceso de descubrimiento de fármacos

Michael Ramirez Campos
, Diana C. Rodriguez
, Alvaro D. Orjuela-Canon

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

Resumen

La tuberculosis es una enfermedad contagiosa considerada como emergencia mundial por la Organización Mundial de la Salud. Uno de los problemas más comunes está asociado a la tuberculosis resistente a los medicamentos, debido a tratamientos fallidos 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 centró en identificar nuevos componentes que puedan contribuir a la inhibición de la proteína inhA. Aprovechando los modelos de 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	Propuesta de compuestos moleculares para la tuberculosis farmacorresistente en el proceso de descubrimiento de fármacos
Idioma original	Inglés estadounidense
Título de la publicación alojada	2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)
Editores	Alvaro David Orjuela-Canon
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	1-5
Número de páginas	5
ISBN (versión digital)	9798350316599
DOI	https://doi.org/10.1109/colcaci59285.2023.10225875
Estado	Publicada - jul 26 2023
Evento	2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia Duración: jul 26 2023 → jul 28 2023

Serie de la publicación

Nombre	2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)

Conferencia

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

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Áreas temáticas de ASJC Scopus

Inteligencia artificial
Informática aplicada
Visión artificial y reconocimiento de patrones
Control y optimización

Acceder al documento

10.1109/colcaci59285.2023.10225875

Otros archivos y enlaces

Citar esto

Campos, M. R., Rodriguez, D. C., & Orjuela-Canon, A. D. (2023). Molecular Compounds Proposal for Drug-Resistant Tuberculosis in the Drug Discovery Process. En A. D. Orjuela-Canon (Ed.), 2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) (pp. 1-5). (2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/colcaci59285.2023.10225875

Campos, Michael Ramirez ; Rodriguez, Diana C. ; Orjuela-Canon, Alvaro D. / Molecular Compounds Proposal for Drug-Resistant Tuberculosis in the Drug Discovery Process. 2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI). editor / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1-5 (2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)).

@inproceedings{30e80f8b12664c889639ea6dd5d1c6c8,

title = "Molecular Compounds Proposal for Drug-Resistant Tuberculosis in the Drug Discovery Process",

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.",

author = "Campos, \{Michael Ramirez\} and Rodriguez, \{Diana C.\} and Orjuela-Canon, \{Alvaro D.\}",

note = "Funding Information: ACKNOWLEDGMENT Authors acknowledge the support of the Universidad del Rosario for funding this project. In addition, the contribution of research incubator team Semillero en Inteligencia Artificial en Salud: Semill-IAS. Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 ; Conference date: 26-07-2023 Through 28-07-2023",

year = "2023",

month = jul,

day = "26",

doi = "10.1109/colcaci59285.2023.10225875",

language = "English (US)",

series = "2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--5",

editor = "Orjuela-Canon, \{Alvaro David\}",

booktitle = "2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)",

address = "United States",

}

Campos, MR, Rodriguez, DC & Orjuela-Canon, AD 2023, Molecular Compounds Proposal for Drug-Resistant Tuberculosis in the Drug Discovery Process. En AD Orjuela-Canon (ed.), 2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI). 2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023, Bogota, Colombia, 7/26/23. https://doi.org/10.1109/colcaci59285.2023.10225875

Molecular Compounds Proposal for Drug-Resistant Tuberculosis in the Drug Discovery Process. / Campos, Michael Ramirez; Rodriguez, Diana C.; Orjuela-Canon, Alvaro D.
2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI). ed. / Alvaro David Orjuela-Canon. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1-5 (2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)).

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

TY - GEN

T1 - Molecular Compounds Proposal for Drug-Resistant Tuberculosis in the Drug Discovery Process

AU - Campos, Michael Ramirez

AU - Rodriguez, Diana C.

AU - Orjuela-Canon, Alvaro D.

N1 - Funding Information: ACKNOWLEDGMENT Authors acknowledge the support of the Universidad del Rosario for funding this project. In addition, the contribution of research incubator team Semillero en Inteligencia Artificial en Salud: Semill-IAS. Publisher Copyright: © 2023 IEEE.

PY - 2023/7/26

Y1 - 2023/7/26

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.

UR - https://www.scopus.com/pages/publications/85171617479

UR - https://www.scopus.com/pages/publications/85171617479#tab=citedBy

U2 - 10.1109/colcaci59285.2023.10225875

DO - 10.1109/colcaci59285.2023.10225875

M3 - Conference contribution

AN - SCOPUS:85171617479

T3 - 2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)

SP - 1

EP - 5

BT - 2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)

A2 - Orjuela-Canon, Alvaro David

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023

Y2 - 26 July 2023 through 28 July 2023

ER -

Campos MR, Rodriguez DC , Orjuela-Canon AD. Molecular Compounds Proposal for Drug-Resistant Tuberculosis in the Drug Discovery Process. En Orjuela-Canon AD, editor, 2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI). Institute of Electrical and Electronics Engineers Inc. 2023. p. 1-5. (2023 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI)). doi: 10.1109/colcaci59285.2023.10225875

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

Resumen

Serie de la publicación

Conferencia

ODS de las Naciones Unidas

Áreas temáticas de ASJC Scopus

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto