Computational Modeling in Nanomedicine: Prediction of Multiple Antibacterial Profiles of Nanoparticles Using a Quantitative Structure–Activity Relationship Perturbation Model

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.

Keywords::

• antibacterial activity
• moving average approach
• nanoparticle
• perturbation
• QSAR

Financial & competing interests disclosure

This work is supported by Grant No. Pest-C/EQB/LA0006/2013, financed by the Portuguese FCT – Fundação para a Ciência e a Tecnologia. The work also received financial support from the European Union (FEDER funds) under the framework of QREN through the project NORTE-07-0124-FEDER-000067-NANOCHEMISTRY. The authors A. Speck-Planche and F. Luan acknowledge also the joint financial support given by the Portuguese FCT, QREN/POPH/MEC, and the European Social Fund (Grants SFRH/BD/77690/2011 and SFRH/BPD/63666/2009, respectively). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Additional information

Funding

This work is supported by Grant No. Pest-C/EQB/LA0006/2013, financed by the Portuguese FCT – Fundação para a Ciência e a Tecnologia. The work also received financial support from the European Union (FEDER funds) under the framework of QREN through the project NORTE-07-0124-FEDER-000067-NANOCHEMISTRY. The authors A. Speck-Planche and F. Luan acknowledge also the joint financial support given by the Portuguese FCT, QREN/POPH/MEC, and the European Social Fund (Grants SFRH/BD/77690/2011 and SFRH/BPD/63666/2009, respectively). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.