Evidence map›Paper›PMID 42853417›Full record

ArticleJournal of computer-aided molecular design2026

Toward FOXM1 inhibitors based on supervised machine learning classification models and similarity-based screening.

Ashutosh Kharwar, Alberto Marbán-González, José L Medina-Franco, Carlos A Velázquez-Martínez

Abstract read
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In one paragraph

Article in Journal of computer-aided molecular design, 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

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

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

4 authors.

Ashutosh KharwarFaculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, T6G 2E1, Canada. akharwar@ualberta.ca.ORCID https://orcid.org/0009-0003-9087-8393
Alberto Marbán-GonzálezDIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510, Mexico City, Mexico.
José L Medina-FrancoDIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, 04510, Mexico City, Mexico.
Carlos A Velázquez-MartínezFaculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, T6G 2E1, Canada. velazque@ualberta.ca.

Funding

Anusandhan National Research Foundation (ANRF), Government of India SB/S9/Z-16/2016-IV (2023)Canadian Institutes of Health Research (CIHR) 481194
6 · The paper itself

Abstract

Ovarian cancer remains among the deadliest gynaecological malignancies, with aberrant activation of the Forkhead Box M1 (FOXM1) transcription factor strongly implicated in tumour progression, therapy resistance, and poor prognosis. Given the limited availability of validated FOXM1 inhibitors and the lack of prior dedicated machine learning-based QSAR studies identified in our literature survey, this study employed a supervised machine learning framework to predict the bioactivity of structurally diverse small-molecule inhibitors. A curated dataset of 101 compounds with reported IC

Indexed as

Antineoplastic AgentsForkhead Box Protein M1Ovarian NeoplasmsSmall Molecule LibrariesSupervised Machine LearningFemaleHumansPrediction AlgorithmsPredictive Learning ModelsQuantitative Structure-Activity RelationshipAntineoplastic AgentsForkhead Box Protein M1FOXM1 protein, humanSmall Molecule LibrariesChemoinformaticsDrug discoveryFOXM1 inhibitorsMachine learningOvarian cancer

Identifiers

PMID42853417

What OpenQuestion holds

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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.