Evidence map›Paper›PMID 40559656›Full record

ArticleMarine drugs2025

A Chemoinformatics Investigation of Spectral and Quantum Chemistry Patterns for Discovering New Drug Leads from Natural Products Targeting the PD-1/PD-L1 Immune Checkpoint, with a Particular Focus on Naturally Occurring Marine Products.

Henrique Rabelo, Ayana Tsimiante, Yuri Binev, Florbela Pereira

Abstract read
In one paragraph

Article in Marine drugs, 2025. 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.

Henrique RabeloLAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
Ayana TsimianteLAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
Yuri BinevLAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
Florbela PereiraLAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.ORCID 0000-0003-4392-4644

Funding

Fundação para a Ciência e Tecnologia CEECIND/01649/2021Fundação para a Ciência e Tecnologia UIDB/50006/2020
6 · The paper itself

Abstract

(1) Background: Although the field of natural product (NP) drug discovery has been extensively developed, there are still several bottlenecks hindering the development of drugs from NPs. The PD-1/PD-L1 immune checkpoint axis plays a crucial role in immune response regulation. Therefore, drugs targeting this axis can disrupt the interaction and enable immune cells to continue setting up a response against the cancer cells. (2) Methods: We have explored the immuno-oncological activity of NPs targeting the PD-1/PD-L1 immune checkpoint by estimating the half maximal inhibitory concentration (IC

Indexed as

B7-H1 AntigenBiological ProductsImmune Checkpoint InhibitorsProgrammed Cell Death 1 ReceptorAquatic OrganismsCheminformaticsDrug DiscoveryHumansMachine LearningMolecular Docking SimulationQuantitative Structure-Activity RelationshipB7-H1 AntigenBiological ProductsCD274 protein, humanImmune Checkpoint InhibitorsPDCD1 protein, humanProgrammed Cell Death 1 Receptorimmuno-oncologymachine learning (ML) techniquesmolecular dockingnatural products (NPs)nuclear magnetic resonance (NMR)PD-1/PD-L1 immune checkpointquantitative structure–activity relationship (QSAR) modelsvirtual screening

Identifiers

PMID40559656
PMCPMC12194479

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Registered trials

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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.