Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Predicting the binding of small molecules to nuclear receptors using machine learning

Briefings in Bioinformatics(2022)10.1093/bib/bbac114Source: DataRank Database

Predicting the binding of small molecules to nuclear receptors using machine learning is a research paper published in Briefings in Bioinformatics (2022). On theSindex it has a DataRank of 0. It has been cited 27 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access27 citations

Download PDF

Cite:

Linked data & code

doi

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)Impact (FWCI 3.16)OA: bronzeTopics (60)IDs (PubMed, PMC, OpenAlex)Annotations (1)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (6)

Martyn T. Smith ORCID,David McCoy ORCID,Alan Hubbard ORCID,Michele A. La Merrill ORCID,Kathleen A. Durkin ORCID

Live enrichment

PubMed:35383362 PMC:PMC9116378 OpenAlex:https://openalex.org/W4226079108

Office of Environmental Health Hazard Assessment of the California Environmental Protection Agency

Grant: 17-0023

Office of Environmental Health Hazard Assessment of the California Environmental Protection Agency

Grant: 17-E0024

USDA National Institute of Food and Agriculture, Hatch project

Grant: 1002182

National Institutes of Health

Grant: S10OD023532

NIEHS NIH HHS

Grant: P42 ES004705

National Institutes of Health

Grant: 1S10OD023532-01

High Performance Computing Cluster for Public Health Driven Molecular Science - Core Facility

FWCI

3.16

Citation Percentile

0.9%

Citation Trend

2022

2023

2024

2025

2026

bronzeLicense: OUP Standard Publication Reuse

journalhttps://academic.oup.com/bib/article-pdf/23/3/bbac114/43745282/bbac114.pdf

publisherhttps://academic.oup.com/bib/article-pdf/23/3/bbac114/43745282/bbac114.pdf

journalhttps://doi.org/10.1093/bib/bbac114

repositoryhttps://pubmed.ncbi.nlm.nih.gov/35383362

repositoryhttps://escholarship.org/uc/item/3p5621nx

Fields of Study

Computational Drug Discovery MethodsReceptor Mechanisms and SignalingChemical Synthesis and AnalysisEndocrine DisruptorsHumansMachine LearningReceptors, Cytoplasmic and Nuclear

MeSH Terms

Machine LearningHumansReceptors, Cytoplasmic and NuclearEndocrine Disruptors

Keywords

Computer scienceMachine learningUploadMolecular descriptorArtificial intelligenceSet (abstract data type)Web serverFunction (biology)Computational biologyThe InternetQuantitative structure–activity relationshipBiologyWorld Wide WebToxicityNuclear receptorSuper Learner5703102 Bioinformatics and computational biology (for-2020)Machine Learning (mesh)Cytoplasmic and NuclearBioinformaticsBioinformatics and Computational Biology0601 Biochemistry and Cell Biology (for)Receptors, Cytoplasmic and NuclearEndocrine Disruptors3105 Genetics (for-2020)ReceptorsGeneticsMachine Learning and Artificial IntelligenceHumans0899 Other Information and Computing Sciences (for)Other Information and Computing Sciences3101 Biochemistry and cell biology (for-2020)Endocrine Disruptors (mesh)Humans (mesh)31 Biological Sciences (for-2020)Networking and Information Technology R&D (NITRD) (rcdc)Generic health relevance (hrcs-hc)0802 Computation Theory and Mathematics (for)Computation Theory and MathematicsBiological SciencesEstrogenNetworking and Information Technology R&D (NITRD)Bioinformatics (science-metrix)Cytoplasmic and Nuclear (mesh)Biochemistry and Cell BiologyGeneric health relevanceMachine Learning and Artificial Intelligence (rcdc)Estrogen (rcdc)

Sustainable Development Goals

SDG 3: 3. Good health