tmQM-RDF Data Set: A Knowledge Graph Representing Transition Metal Complexes.

Luca Cibinel, Trond Linjordet, Johan Pensar, David Balcells, Riccardo De Bin, Basil Ell

Abstract read

In one paragraph

Article in Journal of chemical information and modeling, 2026. 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

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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

6 authors.

Luca CibinelIntegreat─Norwegian Centre for Knowledge-driven Machine Learning, 0851 Oslo, Norway.ORCID 0009-0009-1274-8327

Trond LinjordetDepartment of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway.

Johan PensarIntegreat─Norwegian Centre for Knowledge-driven Machine Learning, 0851 Oslo, Norway.

David BalcellsHylleraas Centre of Excellence for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, 0315 Oslo, Norway.

Riccardo De BinIntegreat─Norwegian Centre for Knowledge-driven Machine Learning, 0851 Oslo, Norway.

Basil EllIntegreat─Norwegian Centre for Knowledge-driven Machine Learning, 0851 Oslo, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transition metal complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one in which machine learning and computational approaches can offer a substantial aid. For this reason, appropriate and accessible data sets, collecting a wide range of information, are required in order to facilitate the effective analysis and investigation of such compounds. This paper contributes to the data modeling effort via the introduction of the

Indexed as

Coordination ComplexesTransition ElementsQuantum MechanicsQuantum TheoryCoordination ComplexesTransition Elements

Identifiers

PMID42340183

DOI10.1021/acs.jcim.6c01281

PMCPMC13370852

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.