Literature DB >> 25600965

Computational modeling in nanomedicine: prediction of multiple antibacterial profiles of nanoparticles using a quantitative structure-activity relationship perturbation model.

Alejandro Speck-Planche1, Valeria V Kleandrova, Feng Luan, Maria Natália D S Cordeiro.   

Abstract

AIMS: We introduce the first quantitative structure-activity relationship (QSAR) perturbation model for probing multiple antibacterial profiles of nanoparticles (NPs) under diverse experimental conditions. MATERIALS &
METHODS: The dataset is based on 300 nanoparticles containing dissimilar chemical compositions, sizes, shapes and surface coatings. In general terms, the NPs were tested against different bacteria, by considering several measures of antibacterial activity and diverse assay times. The QSAR perturbation model was created from 69,231 nanoparticle-nanoparticle (NP-NP) pairs, which were randomly generated using a recently reported perturbation theory approach.
RESULTS: The model displayed an accuracy rate of approximately 98% for classifying NPs as active or inactive, and a new copper-silver nanoalloy was correctly predicted by this model with consensus accuracy of 77.73%.
CONCLUSION: Our QSAR perturbation model can be used as an efficacious tool for the virtual screening of antibacterial nanomaterials.

Entities:  

Keywords:  QSAR; antibacterial activity; moving average approach; nanoparticle; perturbation

Mesh:

Substances:

Year:  2015        PMID: 25600965     DOI: 10.2217/nnm.14.96

Source DB:  PubMed          Journal:  Nanomedicine (Lond)        ISSN: 1743-5889            Impact factor:   5.307


  5 in total

1.  Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies.

Authors:  Supratik Kar; Kavitha Pathakoti; Paul B Tchounwou; Danuta Leszczynska; Jerzy Leszczynski
Journal:  Chemosphere       Date:  2020-09-25       Impact factor: 7.086

2.  Computational and Experimental Approaches to Investigate Lipid Nanoparticles as Drug and Gene Delivery Systems.

Authors:  Chun Chan; Shi Du; Yizhou Dong; Xiaolin Cheng
Journal:  Curr Top Med Chem       Date:  2021       Impact factor: 3.295

3.  Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory.

Authors:  Michael González-Durruthy; Adriano V Werhli; Vinicius Seus; Karina S Machado; Alejandro Pazos; Cristian R Munteanu; Humberto González-Díaz; José M Monserrat
Journal:  Sci Rep       Date:  2017-10-16       Impact factor: 4.379

Review 4.  Practices and Trends of Machine Learning Application in Nanotoxicology.

Authors:  Irini Furxhi; Finbarr Murphy; Martin Mullins; Athanasios Arvanitis; Craig A Poland
Journal:  Nanomaterials (Basel)       Date:  2020-01-08       Impact factor: 5.076

Review 5.  A Review on Applications of Computational Methods in Drug Screening and Design.

Authors:  Xiaoqian Lin; Xiu Li; Xubo Lin
Journal:  Molecules       Date:  2020-03-18       Impact factor: 4.411

  5 in total

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