dc.creator | García Godoy, María Jesús | es |
dc.creator | López Camacho, Esteban | es |
dc.creator | García Nieto, José Manuel | es |
dc.creator | Nebro, Antonio J. | es |
dc.creator | Aldana Montes, José F. | es |
dc.date.accessioned | 2021-05-11T05:31:11Z | |
dc.date.available | 2021-05-11T05:31:11Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | García Godoy, M.J., López Camacho, E., García Nieto, J.M., Nebro, A.J. y Aldana Montes, J.F. (2016). Molecular Docking Optimization in the Context of Multi-Drug Resistant and Sensitive EGFR Mutants. Molecules, 21 (11-1575) | |
dc.identifier.issn | 1420-3049 | es |
dc.identifier.uri | https://hdl.handle.net/11441/108825 | |
dc.description.abstract | The human Epidermal Growth Factor (EGFR) plays an important role in signaling pathways,
such as cell proliferation and migration. Mutations like G719S, L858R, T790M, G719S/T790M or
T790M/L858R can alter its conformation, and, therefore, drug responses from lung cancer patients.
In this context, candidate drugs are being tested and in silico studies are necessary to know how
these mutations affect the ligand binding site. This problem can be tackled by using a multi-objective
approach applied to the molecular docking problem. According to the literature, few studies are
related to the application of multi-objective approaches by minimizing two or more objectives
in drug discovery. In this study, we have used four algorithms (NSGA-II, GDE3, SMPSO and
MOEA/D) to minimize two objectives: the ligand–receptor intermolecular energy and the RMSD
score. We have prepared a set of instances that includes the wild-type EGFR kinase domain and
the same receptor with somatic mutations, and then we assessed the performance of the algorithms
by applying a quality indicator to evaluate the convergence and diversity of the reference fronts.
The MOEA/D algorithm yields the best solutions to these docking problems. The obtained solutions
were analyzed, showing promising results to predict candidate EGFR inhibitors by using this
multi-objective approach. | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación TIN2011-25840 | es |
dc.description.sponsorship | Junta de Andalucía P11-TIC-7529 | es |
dc.description.sponsorship | Junta de Andalucía P12-TIC-1519 | es |
dc.format | application/pdf | es |
dc.format.extent | 14 | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Molecules, 21 (11-1575) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Molecular Docking | es |
dc.subject | Metaheuristics | es |
dc.subject | Multi-objective optimization | es |
dc.subject | Drug resistance | es |
dc.subject | Epidermal growth factor | es |
dc.subject | Epidermal Growth Factor Receptor | es |
dc.subject | Epidermal Growth Factor Receptor mutants | es |
dc.title | Molecular Docking Optimization in the Context of Multi-Drug Resistant and Sensitive EGFR Mutants | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | TIN2011-25840 | es |
dc.relation.projectID | P11-TIC-7529 | es |
dc.relation.projectID | P12-TIC-1519 | es |
dc.relation.publisherversion | https://www.mdpi.com/1420-3049/21/11/1575 | es |
dc.identifier.doi | 10.3390/molecules21111575 | es |
dc.journaltitle | Molecules | es |
dc.publication.volumen | 21 | es |
dc.publication.issue | 11-1575 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Junta de Andalucía | es |