Evidence map›Paper›PMID 26628925›Full record

ArticleJournal of cheminformatics2015

A note on utilising binary features as ligand descriptors.

Hamse Y Mussa, John B O Mitchell, Robert C Glen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cheminformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Hamse Y MussaCentre for Molecular Informatics, Department of Chemistry, Cambridge University, Lensfield Road, Cambridge, CB2 1EW UK ; EaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews, North Haugh, St Andrews, KY16 9ST St Andrews, Scotland.
John B O MitchellEaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews, North Haugh, St Andrews, KY16 9ST St Andrews, Scotland.
Robert C GlenCentre for Molecular Informatics, Department of Chemistry, Cambridge University, Lensfield Road, Cambridge, CB2 1EW UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is common in cheminformatics to represent the properties of a ligand as a string of 1's and 0's, with the intention of elucidating, inter alia, the relationship between the chemical structure of a ligand and its bioactivity. In this commentary we note that, where relevant but non-redundant features are binary, they inevitably lead to a classifier capable of capturing only a linear relationship between structural features and activity. If, instead, we were to use relevant but non-redundant real-valued features, the resulting predictive model would be capable of describing a non-linear structure-activity relationship. Hence, we suggest that real-valued features, where available, are to be preferred in this scenario.

Indexed as

Bernoulli distributionBinary descriptorsLigand chemical structureLinear relationship

Identifiers

PMID26628925
PMCPMC4665894

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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.