Evidence map›Paper›PMID 39359466›Full record

ArticleRoyal Society open science2024

Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations.

Quynh Mai Thai, Minh Quan Pham, Phuong-Thao Tran, Trung Hai Nguyen, Son Tung Ngo

Abstract read
In one paragraph

Article in Royal Society open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Structural Bioinformatics Applied to Acetylcholinesterase Enzyme Inhibition.International journal of molecular sciences · 2025
    Review
  4. Article
  5. 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

5 authors.

Quynh Mai ThaiLaboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam.
Minh Quan PhamFaculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam.ORCID https://orcid.org/0000-0001-6922-1627
Phuong-Thao TranGraduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi 11307, Vietnam.ORCID https://orcid.org/0000-0003-4855-2544
Trung Hai NguyenLaboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam.ORCID https://orcid.org/0000-0003-1848-3963
Son Tung NgoLaboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam.ORCID https://orcid.org/0000-0003-1034-1768

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Targeting acetylcholinesterase is one of the most important strategies for developing therapeutics against Alzheimer's disease. In this work, we have employed a new approach that combines machine learning models, a multi-step similarity search of the PubChem library and molecular dynamics simulations to investigate potential inhibitors for acetylcholinesterase. Our search strategy has been shown to significantly enrich the set of compounds with strong predicted binding affinity to acetylcholinesterase. Both machine learning prediction and binding free energy calculation, based on linear interaction energy, suggest that the compound CID54414454 would bind strongly to acetylcholinesterase and hence is a promising inhibitor.

Indexed as

AChEbinding free energymachine learningMD simulations

Identifiers

PMID39359466
PMCPMC11444763

What Socratic 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 Socratic graph.