FAME 2 site-of-metabolism predictor

Machine learning models for site-of-metabolism prediction
FAME 2 is a cytochrome P450 site-of-metabolism predictor that uses extra trees machine learning models based on circular 2D atomic descriptors.

For end-users
For developers
Application, Software, Trained model, Model, Service
API Type:
Processing and analysis
Applicability domain:
Predictive toxicology
Structure-activity relationship (SAR / QSAR), Predictive modelling
Targeted industry:
Drugs, Cosmetics, Food, Chemicals
Targeted users:
Software Developers, Students, Researchers
Relevant OpenRiskNet case study:
MetaP - Metabolism Prediction
References and training materials:

FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity Martin Šícho, Christina de Bruyn Kops, Conrad Stork, Daniel Svozil, and Johannes Kirchmair Journal of Chemical Information and Modeling 2017 57 (8), 1832-1846 DOI: 10.1021/acs.jcim.7b00250

Provided by:
Universitaet Hamburg and UCT Prague and University of Bergen
Login required:
Implementation status:
API documentation available (Swagger-OpenAPI v2)
Technology readiness level:
TRL 3 – experimental proof of concept
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.