Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles

Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-in...

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Veröffentlicht in:Journal of cheminformatics Jg. 8; H. 1; S. 67
Hauptverfasser: Lampa, Samuel, Alvarsson, Jonathan, Spjuth, Ola
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 24.11.2016
BioMed Central Ltd
Springer Nature B.V
Schlagworte:
Chemistry
Chemistry and Materials Science
Computational Biology/Bioinformatics
Computational chemistry
Computer Applications in Chemistry
Documentation and Information in Chemistry
Drug discovery
Flow-based programming
Machine learning
Methodology
Molecular structure
Predictive modelling
Technology application
Theoretical and Computational Chemistry
Workflows
Flow-based programming
Predictive modelling
Drug discovery
Workflows
Machine learning
ISSN:1758-2946, 1758-2946
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Zusammenfassung:Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster. Graphical abstract .
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ISSN:1758-2946
1758-2946
DOI:10.1186/s13321-016-0179-6