dc.contributor.author | Urista, Diana V. | |
dc.contributor.author | Carrué, Diego B. | |
dc.contributor.author | Otero, Iago | |
dc.contributor.author | Arrasate Gil, Sonia | |
dc.contributor.author | Quevedo Tumailli, Viviana F. | |
dc.contributor.author | Gestal, Marcos | |
dc.contributor.author | González Díaz, Humberto | |
dc.contributor.author | Munteanu, Cristian R. | |
dc.date.accessioned | 2020-09-07T10:48:23Z | |
dc.date.available | 2020-09-07T10:48:23Z | |
dc.date.issued | 2020-07-30 | |
dc.identifier.citation | Biology 9(8) : (2020) // Article ID 198 | es_ES |
dc.identifier.issn | 2079-7737 | |
dc.identifier.uri | http://hdl.handle.net/10810/46001 | |
dc.description.abstract | Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository. | es_ES |
dc.description.sponsorship | This research and the APC were funded by Consolidation and Structuring of Competitive Research
Units—Competitive Reference Groups (ED431C 2018/49) funded by the Ministry of Education, University and Vocational Training of Xunta de Galicia endowed with EU FEDER funds. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | decorated nanoparticles | es_ES |
dc.subject | drug delivery | es_ES |
dc.subject | antimalarial compounds | es_ES |
dc.subject | big data | es_ES |
dc.subject | Perturbation Theory | es_ES |
dc.subject | Machine Learning | es_ES |
dc.subject | ChEMBL database | es_ES |
dc.title | Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2020-08-21T13:48:50Z | |
dc.rights.holder | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2079-7737/9/8/198 | es_ES |
dc.identifier.doi | 10.3390/biology9080198 | |
dc.departamentoes | Química orgánica II | |
dc.departamentoeu | Kimika organikoa II | |