Carbon Nanotubes’ Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra

González-Durruthy, Michael; Monserrat, José M.; Rasulev, Bakhtiyor; Casañola-Martín, Gerardo M.; Sorrivas, José María Barreiro; Paraiso-Medina, Sergio; Maojo, Víctor; González-Dı́az, Humberto; Munteanu, Cristian R.

doi:10.3390/nano7110386

Cited by 14 publications

(9 citation statements)

References 43 publications

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“…In addition, there are also reports of simulations on mixed materials of NC and CNTs [136,137]. In recent years, as a result of remarkable improvements in computer performance, many researches on NC or CNTs using machine learning have been reported [138][139][140][141][142][143][144]. In the future, research using these machine learning methods will accelerate the development of further mixed materials of cellulose and CNTs research, such as by reducing the time and cost of experiments.…”

Section: Prospects For Mixed Materialsmentioning

confidence: 99%

A Review of Applications Using Mixed Materials of Cellulose, Nanocellulose and Carbon Nanotubes

2020

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Carbon nanotubes (CNTs) have been extensively studied as one of the most interesting nanomaterials for over 25 years because they exhibit excellent mechanical, electrical, thermal, optical, and electrical properties. In the past decade, the number of publications and patents on cellulose and nanocellulose (NC) increased tenfold. Research on NC with excellent mechanical properties, flexibility, and transparency is accelerating due to the growing environmental problems surrounding us such as CO2 emissions, the accumulation of large amounts of plastic, and the depletion of energy resources such as oil. Research on mixed materials of cellulose, NC, and CNTs has been expanding because these materials exhibit various characteristics that can be controlled by varying the combination of cellulose, NC to CNTs while also being biodegradable and recyclable. An understanding of these mixed materials is required because these characteristics are diverse and are expected to solve various environmental problems. Thus far, many review papers on cellulose, NC or CNTs have been published. Although guidance for the suitable application of these mixed materials is necessary, there are few reviews summarizing them. Therefore, this review introduces the application and feature on mixed materials of cellulose, NC and CNTs.

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Section: Prospects For Mixed Materialsmentioning

confidence: 99%

A Review of Applications Using Mixed Materials of Cellulose, Nanocellulose and Carbon Nanotubes

2020

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“…In order to mix nanotechnology, chemistry, and data analysis, the PTML method was proposed by combining Perturbation Theory (PT) with Machine Learning (ML) [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Thus, different PT operators can be used to mix the original molecular descriptors with the experimental conditions in order to predict biological activity.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

Munteanu

Gutiérrez-Asorey

Blanes-Rodríguez

et al. 2021

IJMS

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The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors.

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“…Moreover, a brand new method for data fusion in nanotechnology, bio-molecular sciences, chemistry and big data analysis has been proposed in different works: it integrates Perturbation Theory (PT) and Machine Learning (ML) [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], using distinct PT operators to analyze changes in the varied non-structural and structural conditions of a test at once (PTML). A few of these PT operators represent the generalization of a classic cheminformatics approach introduced by Corwin Hansch [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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

Urista

Carrué

Otero

et al. 2020

Biology

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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.

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Carbon Nanotubes’ Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra

Cited by 14 publications

References 43 publications

A Review of Applications Using Mixed Materials of Cellulose, Nanocellulose and Carbon Nanotubes

A Review of Applications Using Mixed Materials of Cellulose, Nanocellulose and Carbon Nanotubes

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

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

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