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Services

State-of-the-Art Nanoinformatics Models & Tools

Our Services

Advanced Nanoinformatics Solutions

Innovative and state-of-the-art nanoinformatics modelling techniques and tools.

Predictive models (QSAR/Read-across)

A large collection of models for the prediction of properties and toxicity end-points are provided as ready-to-use web applications through the NanoSolveIT platform.

Omics Analysis/Molecular Pathways/AOPs

The NanoSolveIT platform offers multiple tools for analysis of gene expression, transcriptomics, and other large-scale datasets, as well as access to molecular pathway and AOP databases.

Biokinetis Models

Several biokinetics models ranging from sinlge compartment to full PBPK models for both humans and enironmental species are provided with graphical user interfaces.

Exposure Models

A number of user-friendly occupational and environmental exposure and fate models for NMs are offered through the NanoSolveIT platform.

Deep Learning / Image Analysis Tools

Deep Learning is a powerful machine learning technology that can extract knowledge and create predictive models using images as input information.

Integrated Approaches to Testing and Assessment (IATAs)

IATAs are approaches for NMs hazard characterisation that rely on an integrated analysis of existing information coupled with the generation of new information using testing strategies.

Databases, Enrichment and Annotation Solutions

(Meta)data curation, enrichment, annotation and ready-for-modelling datasets using structural, molecular and atomistic NMs descriptors.

Our Process

How We Work

We maximise high-quality data exploitation to develop and implement innovative modelling techniques and tools to be integrated within the NanoSolveIT IATA and into a sustainable interoperable product.

Research

Targeted experiments to fill data gaps and increase the quality of existing data.

Develop

Development of robust and validated nanoinformatics tools and services.

Test & Improve

Real-life scenarios and testing and expansion of the models’ applicability domain.