Evidence map›Paper›PMID 35643512›Full record

ArticleJournal of cheminformatics2022

canSAR chemistry registration and standardization pipeline.

Daniela Dolciami, Eloy Villasclaras-Fernandez, Christos Kannas, Mirco Meniconi, Bissan Al-Lazikani, Albert A Antolin

Abstract read
In one paragraph

Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

6 authors.

Daniela DolciamiDepartment of Data Science, The Institute of Cancer Research, London, SM2 5NG, UK.
Eloy Villasclaras-FernandezDepartment of Data Science, The Institute of Cancer Research, London, SM2 5NG, UK.
Christos KannasMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden.
Mirco MeniconiCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London, SM2 5NG, UK.
Bissan Al-LazikaniMD Anderson Cancer Center, Houston, TX, 77054, USA. ballazikani@mdanderson.org.
Albert A AntolinDepartment of Data Science, The Institute of Cancer Research, London, SM2 5NG, UK. Albert.Antolin@icr.ac.uk.

Funding

Cancer Research UK C309/A11566Cancer Research UK C35696/A23187FP7 People: Marie-Curie Actions FP7/2007-2013Wellcome Trust 204735/Z/16/ZWellcome Trust 212969/Z/18/Z
6 · The paper itself

Abstract

backgroundIntegration of medicinal chemistry data from numerous public resources is an increasingly important part of academic drug discovery and translational research because it can bring a wealth of important knowledge related to compounds in one place. However, different data sources can report the same or related compounds in various forms (e.g., tautomers, racemates, etc.), thus highlighting the need of organising related compounds in hierarchies that alert the user on important bioactivity data that may be relevant. To generate these compound hierarchies, we have developed and implemented canSARchem, a new compound registration and standardization pipeline as part of the canSAR public knowledgebase. canSARchem builds on previously developed ChEMBL and PubChem pipelines and is developed using KNIME. We describe the pipeline which we make publicly available, and we provide examples on the strengths and limitations of the use of hierarchies for bioactivity data exploration. Finally, we identify canonicalization enrichment in FDA-approved drugs, illustrating the benefits of our approach.

resultsWe created a chemical registration and standardization pipeline in KNIME and made it freely available to the research community. The pipeline consists of five steps to register the compounds and create the compounds' hierarchy: 1. Structure checker, 2. Standardization, 3. Generation of canonical tautomers and representative structures, 4. Salt strip, and 5. Generation of abstract structure to generate the compound hierarchy. Unlike ChEMBL's RDKit pipeline, we carry out compound canonicalization ahead of getting the parent structure, similar to PubChem's OpenEye pipeline. canSARchem has a lower rejection rate compared to both PubChem and ChEMBL. We use our pipeline to assess the impact of grouping the compounds in hierarchies for bioactivity data exploration. We find that FDA-approved drugs show statistically significant sensitivity to canonicalization compared to the majority of bioactive compounds which demonstrates the importance of this step.

conclusionsWe use canSARchem to standardize all the compounds uploaded in canSAR (> 3 million) enabling efficient data integration and the rapid identification of alternative compound forms with useful bioactivity data. Comparison with PubChem and ChEMBL pipelines evidenced comparable performances in compound standardization, but only PubChem and canSAR canonicalize tautomers and canSAR has a slightly lower rejection rate. Our results highlight the importance of compound hierarchies for bioactivity data exploration. We make canSARchem available under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) at https://gitlab.icr.ac.uk/cansar-public/compound-registration-pipeline .

Indexed as

CanonicalizationcanSARCompound hierarchyFDA-approved drugsKNIMEStandardizationTautomerism

Identifiers

PMID35643512
PMCPMC9148294

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.