Show simple item record

dc.creatorBorrero, Luz Adriana
dc.creatorSanchez Guette, Lilibeth
dc.creatorLopez, Enrique
dc.creatorPineda, Omar
dc.creatorBUELVAS CASTRO, EDGARDO MANUEL
dc.date.accessioned2021-01-29T21:00:17Z
dc.date.available2021-01-29T21:00:17Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7802
dc.description.abstractIt 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.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceProcedia Computer Sciencespa
dc.subjectSupervisedspa
dc.subjectunsupervised learning machinesspa
dc.subjectsupport vector machine (SVM)spa
dc.subjectartificial neural networks (ANN)spa
dc.titlePredicting toxicity properties through machine learningspa
dc.typearticlespa
dcterms.references1 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 22spa
dcterms.references2 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 Sepspa
dcterms.references3 Ekins S Progress in computational toxicology Journal of Pharmacological and Toxicological Methods., 69 (2) (2014), pp. 115-140 Marspa
dcterms.references4 Hecht D Applications of machine learning and computational intelligence to drug discovery and development Drug Development Research., 72 (1) (2011), pp. 53-65 Febspa
dcterms.references5 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 Novspa
dcterms.references6 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].spa
dcterms.references7 Kanehisa Laboratories, «KEGG: Kyoto Encyclopedia of Genes and Genome,» 2018. [En línea]. Available: https://www.genome.jp/kegg/. [Último acceso: 25 07 2018].spa
dcterms.references8 United States Environmental Protection Agency, Appendix F. SMILES Notation Tutorial, Washington D.C., 2017.spa
dcterms.references9 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].spa
dcterms.references10 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].spa
dcterms.references11 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)spa
dcterms.references12 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 Febspa
dcterms.references13 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 28spa
dcterms.references14 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.spa
dcterms.references15 Kyoto Encyclopedia of Genes and Genomes, «KEGG Release Notes, » [En línea].spa
dcterms.references16 Kyoto Encyclopedia of Genes and Genomes, «KEGG release history, » 2018.spa
dcterms.references17 M. Linderman, J. Sorenson, L. Lee, G. Nolan Computational solutions to large-scale data management and analysis Nature Reviews Genetics, 11 (2010), pp. 647-657spa
dcterms.references18 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. 247-249spa
dcterms.references19 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.spa
dcterms.references20 J. Swamidass†, P. Baldi Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval Journal of Chemical Information and Modeling, 47 (1) (2006), pp. 952-964spa
dcterms.references21 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. 1-8spa
dcterms.references22 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.spa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920305317#!spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.03.093


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International