Evidence map›Paper›PMID 42237327›Full record

ArticleBMC biology2026

PepPharmaHub: a cloud-based platform integrating multimodel language architectures with curated data resources for therapeutic peptide discovery.

Dongya Qin, Xiang Qin, Hai Fang, Zheng Wang

Abstract read
In one paragraph

Article in BMC biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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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.

Dongya QinJinfeng Laboratory, Chongqing, 401329, China.
Xiang QinChongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Hai FangShanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. fh12355@rjh.com.cn.
Zheng WangJinfeng Laboratory, Chongqing, 401329, China. biowz@mail.ustc.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTherapeutic peptides represent a rapidly expanding class of drug candidates due to their diverse biological activities and high specificity. However, accurately predicting peptide functions directly from sequence information remains a major challenge in computational peptidomics. Current tools, typically standalone applications or functionally constrained web servers, lack the flexibility and scalability essential for modern peptide discovery workflows. Therefore, it is necessary to develop a cloud-based, no-code platform that enables customizable modeling and high-throughput functional screening of therapeutic peptides.

resultsPepPharmaHub provides a cloud-based, end-to-end platform that integrates advanced sequence-based language modeling with curated benchmark datasets and interactive visualization modules. The platform features a high-throughput screening module powered by a diverse set of 24 models targeting 20 therapeutic properties, alongside a customizable model training pipeline for user-defined screening tasks. Comprehensive benchmarking on 24 public datasets demonstrates that PepPharmaHub matches or surpasses state-of-the-art predictors, significantly improving the efficiency of large-scale peptide screening. Compared with existing public web servers, PepPharmaHub attains a higher, more tightly distributed accuracy on 3475 newly reported bioactive peptides from 1 January 2023 to 1 June 2025 (20 independent tasks), indicating stronger generalization and practical utility.

conclusionsPepPharmaHub enables accurate, high-throughput prediction of peptide functions through customizable deep learning models and a no-code interface. By outperforming existing tools across multiple benchmarks and supporting interpretable sequence analysis, the platform offers a practical solution for accelerating peptide-based drug discovery.

Indexed as

Cloud ComputingComputational BiologyDrug DiscoveryPeptidesSoftwarePeptidesBERTDeep learningTherapeutic peptide discoveryWeb server

Identifiers

PMID42237327
PMCPMC13474599

What Socratic holds

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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 Socratic graph.