Evidence map›Paper›PMID 41859462›Full record

ArticleFrontiers in cellular and infection microbiology2026

Intelligent

Muhammad Aamir, Khosro Rezaee, Maryam Saberi Anari

Abstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 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

3 authors.

Muhammad AamirCollege of Computer Science and Artificial Intelligence, Huanggang Normal University, Huanggang, Hubei, China.
Khosro RezaeeDepartment of Biomedical Engineering, Meybod University, Meybod, Iran.
Maryam Saberi AnariDepartment of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Malaria remains a major global health burden and motivates fast, reliable in silico prioritization of antimalarial (AM) peptide candidates. Designing such peptides is challenging due to the vast search space, scarce or noisy supervision, and potential out-of-distribution miscalibration of computational scores. Prior pipelines typically rank existing sequences rather than generate new candidates under explicit design constraints with calibrated, risk-aware decision rules. Methods: We propose a constraint-guided generate-then-classify framework. A low-data generator-an optimized variant of CTCM-Neo-proposes de novo sequences within APD3-derived windows for net charge, GRAVY, and Boman index. A frozen, temperature-scaled protein language-model classifier (ConformaX-PEP) outputs calibrated probabilities for predicted antimalarial activity and hemolysis, and a split-conformal gate with risk level α=0.1 converts these scores into accept/reject decisions at fixed operating thresholds Results: On the initial 322-sequence corpus (52 AM, 200 unlabeled, 70 positive-like), a held-out evaluation achieves AUROC ≈0.93, AUPRC ≈0.80, and ECE ≈0.03, indicating strong discrimination with low calibration error prior to external testing. The method outperforms strong baselines in convergence speed and reliability. On 210 previously unseen peptides (80 AM, 130 NM), two independent runs achieve 92.86% and 93.33% accuracy with balanced precision and recall and good calibration. Hyperparameter sweeps reveal broad, stable optima, supporting reproducibility. Template-based docking with GalaxyPepDock is used strictly as a hypothesis-generating structural sanity check and does not constitute evidence of biological binding or efficacy. Discussion: Overall, the framework compresses the search space into a small, risk-bounded set of computationally prioritized candidates and provides a scalable, uncertainty-aware route for downstream experimental follow-up. All results reported here are computational, and antimalarial activity remains to be confirmed experimentally.

Indexed as

AntimalarialsDrug DiscoveryPeptidesComputational BiologyComputer SimulationHemolysisHumansMalariaPrediction AlgorithmsAntimalarialsPeptidesantimalarial peptidesconformal predictionCTCM-Neode novo designgeneralizationhyperparameter sensitivitypositive–unlabeled learning

Identifiers

PMID41859462
PMCPMC12996230

What OpenQuestion holds

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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 OpenQuestion graph.