Evidence map›Paper›PMID 33534023›Full record

ArticleMolecular diversity2021

Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms.

Hongzhao Wang, Zijian Qin, Aixia Yan

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular diversity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 37% of its field
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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. 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

3 authors at 1 institution in 1 country.

Hongzhao WangState Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, University of Chemical Technology, Beijing, People's Republic of China.
Zijian QinState Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, University of Chemical Technology, Beijing, People's Republic of China.
Aixia YanState Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, University of Chemical Technology, Beijing, People's Republic of China. yanax@mail.buct.edu.cn.
State Key Laboratory of Chemical Engineering · CN

Funding

National Natural Science Foundation of China 21675010
6 · The paper itself

Abstract

Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 receptor antagonists which were divided into two groups: highly active molecules (IC

Indexed as

AlgorithmsMachine LearningModels, MolecularBinding SitesCheminformaticsDrug DiscoveryLeukotriene AntagonistsMolecular StructureProtein BindingQuantitative Structure-Activity RelationshipReceptors, LeukotrieneReproducibility of ResultsROC CurveLeukotriene Antagonistsleukotriene D4 receptorReceptors, LeukotrieneClassification modelsCysteinyl leukotrienes 1 (CysLT1) receptorDeep neural network (DNN)Morgan fingerprintRandom forest (RF)Recurrent neural network with self-attention (self-attention RNN)Structure–activity relationship (SAR)

Identifiers

PMID33534023
OpenAlexW3127905311

What OpenQuestion holds

Textmetadata
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.