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Article

Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

1
Department of Organic Chemistry II, University of Basque Country (UPV/EHU), Sarriena w/n, 48940 Leioa, Spain
2
RNASA-IMEDIR, Computer Science Faculty, CITIC, University of A Coruna, Campus Elviña s/n, 15071 A Coruña, Spain
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Universidad Estatal Amazónica UEA, Km. 2 1/2 vía Puyo a Tena (paso lateral), Puyo 160150, Pastaza, Ecuador
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Biomedical Research Institute of A Coruña (INIBIC), Hospital Teresa Herrera, Xubias de Arriba 84, 15006 A Coruña, Spain
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IKERBASQUE, Basque Foundation for Science, Alameda Urquijo 36, 48011 Bilbao, Spain
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Basque Centre for Biophysics CSIC-UPVEHU, University of Basque Country UPV/EHU, Barrio Sarriena, 48940 Leioa, Spain
*
Author to whom correspondence should be addressed.
Biology 2020, 9(8), 198; https://doi.org/10.3390/biology9080198
Received: 24 June 2020 / Revised: 22 July 2020 / Accepted: 27 July 2020 / Published: 30 July 2020
(This article belongs to the Special Issue Computational Biology)
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. View Full-Text
Keywords: decorated nanoparticles; drug delivery; antimalarial compounds; big data; Perturbation Theory; Machine Learning; ChEMBL database decorated nanoparticles; drug delivery; antimalarial compounds; big data; Perturbation Theory; Machine Learning; ChEMBL database
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MDPI and ACS Style

Urista, D.V.; Carrué, D.B.; Otero, I.; Arrasate, S.; Quevedo-Tumailli, V.F.; Gestal, M.; González-Díaz, H.; Munteanu, C.R. Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models. Biology 2020, 9, 198. https://doi.org/10.3390/biology9080198

AMA Style

Urista DV, Carrué DB, Otero I, Arrasate S, Quevedo-Tumailli VF, Gestal M, González-Díaz H, Munteanu CR. Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models. Biology. 2020; 9(8):198. https://doi.org/10.3390/biology9080198

Chicago/Turabian Style

Urista, Diana V., Diego B. Carrué, Iago Otero, Sonia Arrasate, Viviana F. Quevedo-Tumailli, Marcos Gestal, Humbert González-Díaz, and Cristian R. Munteanu 2020. "Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models" Biology 9, no. 8: 198. https://doi.org/10.3390/biology9080198

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