FAME 3 site-of-metabolism predictor

Machine learning models for site-of-metabolism prediction
FAME (FAst MEtabolizer) is a machine learning model for the prediction of sites of metabolism (SOMs) for drug-like and other xenobiotic compounds (Šícho et al., 2019). FAME 3 predicts SOMs for phase I, phase II or combined phase I/II metabolism. It uses extra trees machine learning models based on circular 2D atomic descriptors (Šícho et al., 2017). FAME is currently being used at several large pharmaceutical and agrochemical companies. The main components used are the Chemistry Development Kit (CDK) and WEKA, all in JAVA. FAME has a simple command line interface and produces a simple web page and CSV files as output.

For end-users
For developers
Type:
Application, Software, Trained model, Model, Service
API Type:
OpenAPI
Categories:
Processing and analysis
Applicability domain:
Predictive toxicology
Topic:
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:
  • Šícho M, Stork C, Mazzolari A, de Bruyn Kops C, Pedretti A, Testa B, et al. FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes. J Chem Inf Model. 2019;59: 3400–3412. https://doi.org/10.1021/acs.jcim.9b00376
  • Šícho M, de Bruyn Kops C, Stork C, Svozil D, Kirchmair J. FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity. J Chem Inf Model. 2017;57: 1832–1846. DOI: 10.1021/acs.jcim.7b00250
  • Kirchmair J, Mark J. Williamson, Avid M. Afzal, Jonathan D. Tyzack, Alison P. K. Choy, Andrew Howlett, Patrik Rydberg and Robert C. Glen, FAst MEtabolizer (FAME): A Rapid and Accurate Predictor of Sites of Metabolism in Multiple Species by Endogenous Enzymes, J. Chem. Inf. Model. 2013, https://doi.org/10.1021/ci400503s


Provided by:
Universitaet Hamburg and UCT Prague and University of Bergen
Login required:
No
Implementation status:
API documentation available (Swagger-OpenAPI v2), Containerised, Available as web service, Application programming interface available
Technology readiness level:
TRL 3 – experimental proof of concept
Integration status:
Integrated application
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.