Evidence map›Paper›PMID 39427263›Full record

ArticleChemical research in toxicology2024

Machine Learning-Based

Kaori Ambe, Mizuki Nakamori, Riku Tohno, Kotaro Suzuki, Takamitsu Sasaki, Masahiro Tohkin, Kouichi Yoshinari

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Chemical research in toxicology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Applicability domain-expansion studies for machine learning models reveal new inhibitors of CYP2B6.Drug metabolism and disposition: the biological fate of chemicals · 2025
    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

7 authors.

Kaori AmbeDepartment of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 4678603, Japan.ORCID 0000-0003-4387-9134
Mizuki NakamoriDepartment of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 4678603, Japan.
Riku TohnoDepartment of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 4678603, Japan.
Kotaro SuzukiDepartment of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 4678603, Japan.
Takamitsu SasakiLaboratory of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 4228526, Japan.
Masahiro TohkinDepartment of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya 4678603, Japan.
Kouichi YoshinariLaboratory of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka 4228526, Japan.ORCID 0000-0002-2027-2017

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The prediction of cytochrome P450 inhibition by a computational (quantitative) structure-activity relationship approach using chemical structure information and machine learning would be useful for toxicity research as a simple and rapid

Indexed as

Computer SimulationCytochrome P-450 Enzyme InhibitorsCytochrome P-450 Enzyme SystemMachine LearningAnimalsHumansQuantitative Structure-Activity RelationshipRatsCytochrome P-450 Enzyme InhibitorsCytochrome P-450 Enzyme System

Identifiers

PMID39427263
PMCPMC11577419

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.