Evidence map›Paper›PMID 27942268›Full record

ArticleJournal of cheminformatics2016

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

Samuel Lampa, Jonathan Alvarsson, Ola Spjuth

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Article in Journal of cheminformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Towards reproducible computational drug discovery.Journal of cheminformatics · 2020
    Review
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Samuel LampaDepartment of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden.
Jonathan AlvarssonDepartment of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden.
Ola SpjuthDepartment of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden ; Science for Life Laboratory, Uppsala University, Box 3037, 750 03 Uppsala, Sweden.ORCID 0000-0002-8083-2864

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Drug discoveryFlow-based programmingMachine learningPredictive modellingWorkflows

Identifiers

PMID27942268
PMCPMC5123367

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.