Literature DB >> 33875019

Flame: an open source framework for model development, hosting, and usage in production environments.

Manuel Pastor1, José Carlos Gómez-Tamayo2, Ferran Sanz2.   

Abstract

This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

Entities:  

Keywords:  In-silico toxicology; Model integration; Model management; Modeling framework; Modeling tools; QSAR; Reproducibility; Web-interfaces; Workflow

Year:  2021        PMID: 33875019     DOI: 10.1186/s13321-021-00509-z

Source DB:  PubMed          Journal:  J Cheminform        ISSN: 1758-2946            Impact factor:   5.514


  24 in total

Review 1.  QSAR without borders.

Authors:  Eugene N Muratov; Jürgen Bajorath; Robert P Sheridan; Igor V Tetko; Dmitry Filimonov; Vladimir Poroikov; Tudor I Oprea; Igor I Baskin; Alexandre Varnek; Adrian Roitberg; Olexandr Isayev; Stefano Curtarolo; Denis Fourches; Yoram Cohen; Alan Aspuru-Guzik; David A Winkler; Dimitris Agrafiotis; Artem Cherkasov; Alexander Tropsha
Journal:  Chem Soc Rev       Date:  2020-05-01       Impact factor: 54.564

2.  QSAR modeling: where have you been? Where are you going to?

Authors:  Artem Cherkasov; Eugene N Muratov; Denis Fourches; Alexandre Varnek; Igor I Baskin; Mark Cronin; John Dearden; Paola Gramatica; Yvonne C Martin; Roberto Todeschini; Viviana Consonni; Victor E Kuz'min; Richard Cramer; Romualdo Benigni; Chihae Yang; James Rathman; Lothar Terfloth; Johann Gasteiger; Ann Richard; Alexander Tropsha
Journal:  J Med Chem       Date:  2014-01-06       Impact factor: 7.446

Review 3.  Applications of machine learning in drug discovery and development.

Authors:  Jessica Vamathevan; Dominic Clark; Paul Czodrowski; Ian Dunham; Edgardo Ferran; George Lee; Bin Li; Anant Madabhushi; Parantu Shah; Michaela Spitzer; Shanrong Zhao
Journal:  Nat Rev Drug Discov       Date:  2019-06       Impact factor: 84.694

4.  Legacy data sharing to improve drug safety assessment: the eTOX project.

Authors:  Ferran Sanz; François Pognan; Thomas Steger-Hartmann; Carlos Díaz; Montserrat Cases; Manuel Pastor; Philippe Marc; Joerg Wichard; Katharine Briggs; David K Watson; Thomas Kleinöder; Chihae Yang; Alexander Amberg; Maria Beaumont; Anthony J Brookes; Søren Brunak; Mark T D Cronin; Gerhard F Ecker; Sylvia Escher; Nigel Greene; Antonio Guzmán; Anne Hersey; Pascale Jacques; Lieve Lammens; Jordi Mestres; Wolfgang Muster; Helle Northeved; Marc Pinches; Javier Saiz; Nicolas Sajot; Alfonso Valencia; Johan van der Lei; Nico P E Vermeulen; Esther Vock; Gerhard Wolber; Ismael Zamora
Journal:  Nat Rev Drug Discov       Date:  2017-10-13       Impact factor: 84.694

5.  The ChEMBL database in 2017.

Authors:  Anna Gaulton; Anne Hersey; Michał Nowotka; A Patrícia Bento; Jon Chambers; David Mendez; Prudence Mutowo; Francis Atkinson; Louisa J Bellis; Elena Cibrián-Uhalte; Mark Davies; Nathan Dedman; Anneli Karlsson; María Paula Magariños; John P Overington; George Papadatos; Ines Smit; Andrew R Leach
Journal:  Nucleic Acids Res       Date:  2016-11-28       Impact factor: 16.971

Review 6.  Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.

Authors:  Dmitry Grapov; Johannes Fahrmann; Kwanjeera Wanichthanarak; Sakda Khoomrung
Journal:  OMICS       Date:  2018-08-20

Review 7.  Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.

Authors:  Ahmet Sureyya Rifaioglu; Heval Atas; Maria Jesus Martin; Rengul Cetin-Atalay; Volkan Atalay; Tunca Doğan
Journal:  Brief Bioinform       Date:  2019-09-27       Impact factor: 11.622

8.  The FAIR Guiding Principles for scientific data management and stewardship.

Authors:  Mark D Wilkinson; Michel Dumontier; I Jsbrand Jan Aalbersberg; Gabrielle Appleton; Myles Axton; Arie Baak; Niklas Blomberg; Jan-Willem Boiten; Luiz Bonino da Silva Santos; Philip E Bourne; Jildau Bouwman; Anthony J Brookes; Tim Clark; Mercè Crosas; Ingrid Dillo; Olivier Dumon; Scott Edmunds; Chris T Evelo; Richard Finkers; Alejandra Gonzalez-Beltran; Alasdair J G Gray; Paul Groth; Carole Goble; Jeffrey S Grethe; Jaap Heringa; Peter A C 't Hoen; Rob Hooft; Tobias Kuhn; Ruben Kok; Joost Kok; Scott J Lusher; Maryann E Martone; Albert Mons; Abel L Packer; Bengt Persson; Philippe Rocca-Serra; Marco Roos; Rene van Schaik; Susanna-Assunta Sansone; Erik Schultes; Thierry Sengstag; Ted Slater; George Strawn; Morris A Swertz; Mark Thompson; Johan van der Lei; Erik van Mulligen; Jan Velterop; Andra Waagmeester; Peter Wittenburg; Katherine Wolstencroft; Jun Zhao; Barend Mons
Journal:  Sci Data       Date:  2016-03-15       Impact factor: 6.444

9.  PubChem Substance and Compound databases.

Authors:  Sunghwan Kim; Paul A Thiessen; Evan E Bolton; Jie Chen; Gang Fu; Asta Gindulyte; Lianyi Han; Jane He; Siqian He; Benjamin A Shoemaker; Jiyao Wang; Bo Yu; Jian Zhang; Stephen H Bryant
Journal:  Nucleic Acids Res       Date:  2015-09-22       Impact factor: 16.971

10.  Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility.

Authors:  Thomas Luechtefeld; Dan Marsh; Craig Rowlands; Thomas Hartung
Journal:  Toxicol Sci       Date:  2018-09-01       Impact factor: 4.849

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  1 in total

1.  Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry.

Authors:  Manuel Pastor; Ferran Sanz; Frank Bringezu
Journal:  Methods Mol Biol       Date:  2022
  1 in total

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