Nano-QSAR to predict cytotoxicity of metal and metal oxide nanoparticles

Korea Institute of Technology NanoQSAR model
KIT (Korea Institute of Toxicology) Nano-QSAR models were implemented for prediction of cytotoxicity caused by metal and metal oxide nanoparticles. A Binary classification model was built using the Logistic regression algorithm. The structures involved are metal and metal oxide cluster structures (prepared by Material studio visualizer) and hydroxylated metal ions. The descriptors involved are quantum mechanical descriptors, calculated from MOPAC (PM7). Since quantum mechanical descriptors and molecular structure for the nanoparticle cluster both were prepared by commercial software, here, the model was implemented with descriptors already calculated for numerous metal and metal oxide nanoparticles.

For end-users
For developers
Trained model, Model
API Type:
REST, OpenAPI, REST under OAS3 specification, OAS3, Based on Jaqpot API that uses REST under OpenAPI3 specification
Predictive toxicology
Applicability domain:
Computational modelling, Toxicology, Predictive toxicology
Nano safety, Risk assessment, Structure-activity relationship (SAR / QSAR), Predictive modelling
Targeted industry:
Targeted users:
Risk assessors, Researchers, Students, Software Developers, Data managers, Regulators
Relevant OpenRiskNet case study:
ModelRX - Modelling for Prediction or Read Across
References and training materials:

H. K. Shin, K. Y. Kim, J. W. Park & K. T. No Use of metal/metal oxide spherical cluster and hydroxyl metal coordination complex for descriptor calculation in development of nanoparticle cytotoxicity classification model SAR and QSAR in Environmental Research, 2017 VOL . 28, NO . 11, 875–888

Provided by:
Korea Institute of Toxicology (KIT)
Login required:
Implementation status:
API documentation available (Swagger-OpenAPI v2), OAS v3, Available as web service, Application programming interface available
Technology readiness level:
TRL 7 – system prototype demonstration in operational environment
Integration status:
Integrated application

Resources & Training

Case Study report - Modelling for Prediction or Read Across [ModelRX]
Harry Sarimveis (NTUA)
11 Dec 2019
The ModelRX case study was designed to cover the important area of generating and applying predictive models, and more specifically QSAR models in hazard assessment endorsed by different regulations, as completely in silico alternatives to animal testing and useful also in early research when no data is available for a compound. The QSAR development process schematically presented in Figure 1 begins by obtaining a training data set from an OpenRiskNet data source. A model can then be trained with OpenRiskNet modelling tools and the resulting models are packaged into a container, documented and ontologically annotated. To assure the quality of the models, they are validated using OECD guidelines (Jennings et al. 2018). Prediction for new compounds can be obtained using a specific model or a consensus of predictions of all models. This case study will present this workflow with the example of blood-brain-barrier (BBB) penetration, for which multiple models were generated using tools from OpenRiskNet consortium and associated partners used individually as well as in a consensus approach using Dempster-Shafer theory (Park et al. 2014; Rathman et al. 2018).
Additional materials:

Target audience: Risk assessors, Researchers, Students, OpenRiskNet stakeholders, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, NTUA, UU
OpenRiskNet Part III: Modelling Services in Chemical/Nano-safety, Environmental Science and Pharmacokinetics
Stefan Kramer, Philip Doganis, Denis Gebele, Atif Raza, Pantelis Karatzas, Haralambos Sarimveis, Jonathan Alvarsson, Ola Spjuth, Staffan Arvidsson, Thomas Exner, Lucian Farcal, Barry Hardy
28 Aug 2019
The OpenRiskNet project ( is funded by the H2020-EINFRA-22-2016 Programme and its main objective is the development of an open e-infrastructure providing data and software resources and services to a variety of industries requiring risk assessment (e.g. chemicals, cosmetic ingredients, pharma or nanotechnologies). The concept of case studies was followed in order to test and evaluate proposed solutions and is described in Two case studies, namely ModelRX and RevK, focus on modelling within risk assessment. The ModelRX – Modelling for Prediction or Read Across case study provides computational methods for predictive modelling and support of existing data suitability assessment. It supports final risk assessment by providing calculations of theoretical descriptors, gap filling of incomplete datasets. computational modelling (QSAR) and predictions of adverse effects. Services are offered through Jaqpot (UI/API), JGU WEKA (API), Lazar (UI) and Jupyter & Squonk Notebooks. In the RevK – Reverse dosimetry and PBPK prediction case study, physiologically based pharmacokinetic (PBPK) models are made accessible for the purpose of risk assessment-relevant scenarios. The PKSim software, the httk R package and custom-made PBPK models have been integrated. RevK offers services through Jaqpot (UI/API).

Target audience: Risk assessors, Researchers, Students, Nanosafety community, Data modellers, Bioinformaticians
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: EwC, JGU, NTUA, UU
Model RX OpenRiskNet - Case study using Jaqpot web modelling platform
Philip Doganis
15 Oct 2018

Target audience: Risk assessors, Researchers, OpenRiskNet stakeholders, Data modellers
Open access: yes
Licence: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Organisations involved: NTUA