Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models
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Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest ModelsAuthor(s)
Date
2020-07Citation
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
Abstract
[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.
Keywords
Decorated nanoparticles
Drug delivery
Antimalarial compounds
Big data
Perturbation Theory
Machine Learning
ChEMBL database
Drug delivery
Antimalarial compounds
Big data
Perturbation Theory
Machine Learning
ChEMBL database
Editor version
Rights
Atribución 4.0 Internacional
ISSN
2079-7737