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Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0001-6740-9212
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-8083-2864
2016 (English)In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 8, article id 67Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. Vol. 8, article id 67
Keyword [en]
Predictive modelling, Machine learning, Workflows, Drug discovery, Flow-based programming
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:uu:diva-315089DOI: 10.1186/s13321-016-0179-6ISI: 000391703900001OAI: oai:DiVA.org:uu-315089DiVA, id: diva2:1073204
Funder
eSSENCE - An eScience CollaborationSwedish eā€Science Research CenterSwedish National Infrastructure for Computing (SNIC), b2013262
Available from: 2017-02-09 Created: 2017-02-09 Last updated: 2018-05-18Bibliographically approved

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Lampa, SamuelAlvarsson, JonathanSpjuth, Ola
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