Fragnet Search
Chemical similarity using the Fragment Network
This is the same API that is behind Informatics Matters' Fragnet Search application for which more information can be found at https://fragnet.informaticsmatters.com/
The service deployed to OpenRiskNet is the REST API that sits behind the Fragnet Search application, and allows molecules similar to a query molecule to be fetched from other applications running in the infrastructure.
A Jupyter notebook exemplifying this can be found at https://github.com/OpenRiskNet/notebooks/blob/master/DataCure/Fragnet/fragnet-search.ipynb
For end-users
For developers
Type:
Database / data source, Service
API Type:
OpenAPI, REST, REST under OpenAPI2 specification
Categories:
Processing and analysis
Applicability domain:
Computational modelling, Toxicology, Predictive toxicology
Topic:
Chemical properties, Structure-activity relationship (SAR / QSAR), Predictive modelling
Targeted industry:
Drugs, Cosmetics, Chemicals
Targeted users:
Software Developers, Researchers
Relevant OpenRiskNet case studies:
Documentation:
Provided by:
Informatics Matters Ltd
Licence:
Apache 2.0
Login required:
Yes
Implementation status:
API documentation available (Swagger-OpenAPI v2), Containerised, Available as web service
Technology readiness level:
TRL 6 – technology demonstrated in relevant environment (industrially relevant environment in the case of key enabling technologies)
Integration status:
Integration in progress
Service integration operations completed:
Utilises the OpenRiskNet APIs to ensure that each service is accessible to our proposed interoperability layer.
Is annotated according to the semantic interoperability layer concept using defined ontologies.
Is containerised for easy deployment in virtual environments of OpenRiskNet instances.
Has documented scientific and technical background.
Is deployed into the OpenRiskNet reference environment.
Is listed in the OpenRiskNet discovery services.
Is listed in other central repositories like eInfraCentral, bio.tools and TeSS (ELIXIR).
Provides legal and ethical statements on how the service can be used.
Resources & Training
OpenRiskNet Part IV: WEKA Machine Learning Services for the Prediction of Half-Lifes of Chemicals and Nanoparticle Transport
28 Aug 2019
←
Abstract:
The OpenRiskNet project (https://openrisknet.org/) 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). We will present the WEKA machine learning services within the infrastructure and how they can be used to solve complex prediction tasks: the prediction of (i) half-life of chemicals under given environmental conditions and of (ii) nanoparticle transport behavior from physicochemical properties. For that purpose, we will reconstruct previous efforts using complex workflows and architectures and simplify the models while maintaining their prediction performance. In both cases, the overall problem (predicting the fate of a compound depending on its properties and external conditions) is modeled as a cascaded prediction model, where the prediction of one model is, with particular attention to validity and performance, entering another model as input. The approach performs well on the half-life data, while the nanoparticle data are too noisy and incomplete to warrant more than the most basic models. Overall, the reconstruction of the two applications within OpenRiskNet provides more evidence for the power and versatility of the framework.
The OpenRiskNet project (https://openrisknet.org/) 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). We will present the WEKA machine learning services within the infrastructure and how they can be used to solve complex prediction tasks: the prediction of (i) half-life of chemicals under given environmental conditions and of (ii) nanoparticle transport behavior from physicochemical properties. For that purpose, we will reconstruct previous efforts using complex workflows and architectures and simplify the models while maintaining their prediction performance. In both cases, the overall problem (predicting the fate of a compound depending on its properties and external conditions) is modeled as a cascaded prediction model, where the prediction of one model is, with particular attention to validity and performance, entering another model as input. The approach performs well on the half-life data, while the nanoparticle data are too noisy and incomplete to warrant more than the most basic models. Overall, the reconstruction of the two applications within OpenRiskNet provides more evidence for the power and versatility of the framework.
Target audience: Risk assessors, Researchers, Nanosafety community, Data modellers
Open access: yes
Licence: Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Organisations involved: JGU