Predicting toxicity properties through machine learning
It is currently known that the high power of a drug does not fully determine its efficacy. Several properties must also be considered, including absorption, distribution, metabolism, excretion and toxicity [8]. These are the ADME-Tox properties, which are fundamental in the discovery of new effectiv...
- Autores:
-
Borrero, Luz Adriana
Sanchez Guette, Lilibeth
Lopez, Enrique
Pineda, Omar
BUELVAS CASTRO, EDGARDO MANUEL
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7802
- Acceso en línea:
- https://hdl.handle.net/11323/7802
https://doi.org/10.1016/j.procs.2020.03.093
https://repositorio.cuc.edu.co/
- Palabra clave:
- Supervised
unsupervised learning machines
support vector machine (SVM)
artificial neural networks (ANN)
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Predicting toxicity properties through machine learning |
| title |
Predicting toxicity properties through machine learning |
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Predicting toxicity properties through machine learning Supervised unsupervised learning machines support vector machine (SVM) artificial neural networks (ANN) |
| title_short |
Predicting toxicity properties through machine learning |
| title_full |
Predicting toxicity properties through machine learning |
| title_fullStr |
Predicting toxicity properties through machine learning |
| title_full_unstemmed |
Predicting toxicity properties through machine learning |
| title_sort |
Predicting toxicity properties through machine learning |
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Borrero, Luz Adriana Sanchez Guette, Lilibeth Lopez, Enrique Pineda, Omar BUELVAS CASTRO, EDGARDO MANUEL |
| dc.contributor.author.spa.fl_str_mv |
Borrero, Luz Adriana Sanchez Guette, Lilibeth Lopez, Enrique Pineda, Omar BUELVAS CASTRO, EDGARDO MANUEL |
| dc.subject.spa.fl_str_mv |
Supervised unsupervised learning machines support vector machine (SVM) artificial neural networks (ANN) |
| topic |
Supervised unsupervised learning machines support vector machine (SVM) artificial neural networks (ANN) |
| description |
It is currently known that the high power of a drug does not fully determine its efficacy. Several properties must also be considered, including absorption, distribution, metabolism, excretion and toxicity [8]. These are the ADME-Tox properties, which are fundamental in the discovery of new effective and safe drugs. Since ignoring these properties is the main cause of failure in the development of new drugs, it is understandable that some techniques arise, such as machine learning, which apply some predictor variables as molecular characteristics to obtain models to determine some of these ADME-Tox properties. In silico models are booming because of the exorbitant expenses involved in discovering a new drug using traditional trial-and-error methods [2], and they have proven to be an effective approach to increase efficiency in drug discovery and development processes. The objective of this study is to analyze the best current machine learning techniques for predicting toxicity as an ADME-Tox property. |
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2020 |
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2021-01-29T21:00:17Z |
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2021-01-29T21:00:17Z |
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https://doi.org/10.1016/j.procs.2020.03.093 |
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Corporación Universidad de la Costa |
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eng |
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1 Clark AM, Dole K, Coulon-Spektor A, McNutt A, Grass G, Freundlich JS, et al. Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets Journal of Chemical Information and Modeling, 55 (6) (2015), pp. 1231-1245 Jun 22 2 Ekins S, Mestres J, Testa B In silico pharmacology for drug discovery: applications to targets and beyond British Journal of Pharmacology., 152 (1) (2007), pp. 21-37 Sep 3 Ekins S Progress in computational toxicology Journal of Pharmacological and Toxicological Methods., 69 (2) (2014), pp. 115-140 Mar 4 Hecht D Applications of machine learning and computational intelligence to drug discovery and development Drug Development Research., 72 (1) (2011), pp. 53-65 Feb 5 Hou T, Wang J, Li Y ADME Evaluation in Drug Discovery. 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine Journal of Chemical Information and Modeling, 47 (6) (2007), pp. 2408-2415 Nov 6 International Multimedia Resource Center, «RAM vs. Hard Drive Memory, » 2018. [En línea]. Available: https://www.lehigh.edu/~inimr/computer-basics- tutorial/ramvsdiskspacehtm.htm. [Último acceso: 13 noviembre 2018]. 7 Kanehisa Laboratories, «KEGG: Kyoto Encyclopedia of Genes and Genome,» 2018. [En línea]. Available: https://www.genome.jp/kegg/. [Último acceso: 25 07 2018]. 8 United States Environmental Protection Agency, Appendix F. SMILES Notation Tutorial, Washington D.C., 2017. 9 United States Environmental Protection Agency, «SMILES Tutorial,» 21 febrero 2016. [En línea]. Available: https://archive.epa.gov/med/med_archive_03/web/html/smiles.html. [Último acceso: 26 Julio 2018]. 10 Daylight Chemical Information Systems, «4. SMARTS - A Language for Describing Molecular Patterns, » 2008. [En línea]. Available: http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html. [Último acceso: 26 Julio 2018]. 11 Lantz B Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications, Packt Publ, Birmingham (2013) 12 Maltarollo VG, Gertrudes JC, Oliveira PR, Honorio KM Applying machine learning techniques for ADME-Tox prediction: a review Expert Opinion on Drug Metabolism & Toxicology., 11 (2) (2015), pp. 259-271 Feb 13 Shen J, Cheng F, Xu Y, Li W, Tang Y Estimation of ADME Properties with Substructure Pattern Recognition Journal of Chemical Information and Modeling., 50 (6) (2010), pp. 1034-1041 Jun 28 14 Bucci, N., Luna, M., Viloria, A., García, J.H., Parody, A., Varela, N., & López, L.A.B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham. 15 Kyoto Encyclopedia of Genes and Genomes, «KEGG Release Notes, » [En línea]. 16 Kyoto Encyclopedia of Genes and Genomes, «KEGG release history, » 2018. 17 M. Linderman, J. Sorenson, L. Lee, G. Nolan Computational solutions to large-scale data management and analysis Nature Reviews Genetics, 11 (2010), pp. 647-657 18 L. Wang, X. Qung Xie Computational target fishing: what should chemogenomics researchers expect for the future of in silico drug design and discovery? Future Med Chem, 6 (3) (2014), pp. 247-249 19 Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J.P., Parody, A., Bent, D.E.S., & López, L.A.B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham. 20 J. Swamidass†, P. Baldi Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval Journal of Chemical Information and Modeling, 47 (1) (2006), pp. 952-964 21 S. Arif, J. Holliday, P. Willett Comparison of chemical similarity measures using different numbers of query structures Journal of Information Science, 39 (1) (2013), pp. 1-8 22 Gamero, W.M., Ramírez, M.C., Parody, A., Viloria, A., López, M.H.A., & Kamatkar, S.J. (2018, June). Concentrations and size distributions of fungal bioaerosols in a municipal landfill. In International Conference on Data Mining and Big Data (pp. 244-253). Springer, Cham. |
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Borrero, Luz AdrianaSanchez Guette, LilibethLopez, EnriquePineda, OmarBUELVAS CASTRO, EDGARDO MANUEL2021-01-29T21:00:17Z2021-01-29T21:00:17Z2020https://hdl.handle.net/11323/7802https://doi.org/10.1016/j.procs.2020.03.093Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/It is currently known that the high power of a drug does not fully determine its efficacy. Several properties must also be considered, including absorption, distribution, metabolism, excretion and toxicity [8]. These are the ADME-Tox properties, which are fundamental in the discovery of new effective and safe drugs. Since ignoring these properties is the main cause of failure in the development of new drugs, it is understandable that some techniques arise, such as machine learning, which apply some predictor variables as molecular characteristics to obtain models to determine some of these ADME-Tox properties. In silico models are booming because of the exorbitant expenses involved in discovering a new drug using traditional trial-and-error methods [2], and they have proven to be an effective approach to increase efficiency in drug discovery and development processes. The objective of this study is to analyze the best current machine learning techniques for predicting toxicity as an ADME-Tox property.Borrero, Luz AdrianaSanchez Guette, LilibethLopez, EnriquePineda, Omar-will be generated-orcid-0000-0002-8239-3906-600BUELVAS CASTRO, EDGARDO MANUEL-will be generated-orcid-0000-0002-3097-1997-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920305317#!Supervisedunsupervised learning machinessupport vector machine (SVM)artificial neural networks (ANN)Predicting toxicity properties through machine learningArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1 Clark AM, Dole K, Coulon-Spektor A, McNutt A, Grass G, Freundlich JS, et al. Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets Journal of Chemical Information and Modeling, 55 (6) (2015), pp. 1231-1245 Jun 222 Ekins S, Mestres J, Testa B In silico pharmacology for drug discovery: applications to targets and beyond British Journal of Pharmacology., 152 (1) (2007), pp. 21-37 Sep3 Ekins S Progress in computational toxicology Journal of Pharmacological and Toxicological Methods., 69 (2) (2014), pp. 115-140 Mar4 Hecht D Applications of machine learning and computational intelligence to drug discovery and development Drug Development Research., 72 (1) (2011), pp. 53-65 Feb5 Hou T, Wang J, Li Y ADME Evaluation in Drug Discovery. 8. The Prediction of Human Intestinal Absorption by a Support Vector Machine Journal of Chemical Information and Modeling, 47 (6) (2007), pp. 2408-2415 Nov6 International Multimedia Resource Center, «RAM vs. Hard Drive Memory, » 2018. [En línea]. Available: https://www.lehigh.edu/~inimr/computer-basics- tutorial/ramvsdiskspacehtm.htm. [Último acceso: 13 noviembre 2018].7 Kanehisa Laboratories, «KEGG: Kyoto Encyclopedia of Genes and Genome,» 2018. [En línea]. Available: https://www.genome.jp/kegg/. [Último acceso: 25 07 2018].8 United States Environmental Protection Agency, Appendix F. SMILES Notation Tutorial, Washington D.C., 2017.9 United States Environmental Protection Agency, «SMILES Tutorial,» 21 febrero 2016. [En línea]. Available: https://archive.epa.gov/med/med_archive_03/web/html/smiles.html. [Último acceso: 26 Julio 2018].10 Daylight Chemical Information Systems, «4. SMARTS - A Language for Describing Molecular Patterns, » 2008. [En línea]. Available: http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html. [Último acceso: 26 Julio 2018].11 Lantz B Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications, Packt Publ, Birmingham (2013)12 Maltarollo VG, Gertrudes JC, Oliveira PR, Honorio KM Applying machine learning techniques for ADME-Tox prediction: a review Expert Opinion on Drug Metabolism & Toxicology., 11 (2) (2015), pp. 259-271 Feb13 Shen J, Cheng F, Xu Y, Li W, Tang Y Estimation of ADME Properties with Substructure Pattern Recognition Journal of Chemical Information and Modeling., 50 (6) (2010), pp. 1034-1041 Jun 2814 Bucci, N., Luna, M., Viloria, A., García, J.H., Parody, A., Varela, N., & López, L.A.B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham.15 Kyoto Encyclopedia of Genes and Genomes, «KEGG Release Notes, » [En línea].16 Kyoto Encyclopedia of Genes and Genomes, «KEGG release history, » 2018.17 M. Linderman, J. Sorenson, L. Lee, G. Nolan Computational solutions to large-scale data management and analysis Nature Reviews Genetics, 11 (2010), pp. 647-65718 L. Wang, X. Qung Xie Computational target fishing: what should chemogenomics researchers expect for the future of in silico drug design and discovery? Future Med Chem, 6 (3) (2014), pp. 247-24919 Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J.P., Parody, A., Bent, D.E.S., & López, L.A.B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham.20 J. Swamidass†, P. Baldi Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval Journal of Chemical Information and Modeling, 47 (1) (2006), pp. 952-96421 S. Arif, J. Holliday, P. Willett Comparison of chemical similarity measures using different numbers of query structures Journal of Information Science, 39 (1) (2013), pp. 1-822 Gamero, W.M., Ramírez, M.C., Parody, A., Viloria, A., López, M.H.A., & Kamatkar, S.J. (2018, June). Concentrations and size distributions of fungal bioaerosols in a municipal landfill. In International Conference on Data Mining and Big Data (pp. 244-253). 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