Evidence map›Paper›PMID 42463657›Full record

ArticleNature communications2026

PeptiVerse: A unified platform for therapeutic peptide property prediction.

Yinuo Zhang, Sophia Tang, Tong Chen, Elizabeth Mahood, Sophia Vincoff, Pranam Chatterjee

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yinuo ZhangDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
Sophia TangDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
Tong ChenDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
Elizabeth MahoodDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Sophia VincoffDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Pranam ChatterjeeDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA. pranam@upenn.edu.ORCID 0000-0003-3957-8478

Funding

Towards the Design of Programmable, Isoform-Selective Proteome Editing SystemR35GM155282 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI Pranam Chatterjee · 2024 to 2026
$1.2M
NIGMS NIH HHS R35 GM155282
6 · The paper itself

Abstract

Therapeutic peptides combine the advantages of small molecules and antibodies, offering target flexibility and low immunogenicity, yet their successful translation requires careful evaluation of multiple developability properties beyond binding alone. As chemically modified peptides become increasingly common in drug design, no unified platform currently supports systematic property assessment across both canonical sequences and SMILES-based representations. Leveraging the generalizability of large foundational models trained on protein and chemical data, we introduce PeptiVerse, a universal therapeutic peptide property prediction platform. PeptiVerse accepts either amino acid sequences or chemically modified peptide SMILES, delivers state-of-the-art performance across diverse property prediction tasks, and provides both a web interface and open-source implementation for rapid, accessible, and scalable peptide developability analysis. By unifying property prediction across representations, PeptiVerse directly supports early-stage peptide therapeutic development campaigns and property-aware generative design workflows.

Indexed as

PeptidesSoftwareAmino Acid SequenceDrug DesignPrediction AlgorithmsPeptides

Identifiers

PMID42463657
PMCPMC13388690

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.