Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 48 min ago
Peer-reviewedCC BY-NC-NDNature Communications

PeptiVerse: A unified platform for therapeutic peptide property prediction

  • Yinuo Zhang1,2
  • Sophia Tang2
  • Tong Chen2
  • Elizabeth Mahood3
  • Sophia Vincoff3
  • Pranam Chatterjee4,5
5 affiliations
  1. 1Centre for Biomedical Data Science, Duke-NUS Medical School, Singapore, Singapore
  2. 2Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
  3. 3Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
  4. 4Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA. [email protected]
  5. 5Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA. [email protected]
Open at sourcePDFDOICite

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 pep-tide developability analysis. By unifying property prediction across representations, PeptiVerse directly supports early-stage peptide therapeutic development campaigns and property-aware generative design workflows.

Related

  1. Topology-Guided Multi-Level Graph Learning: A Unified Framework for Molecular Property Prediction
    Hasnain Ali, Momina Arshad, Malik Ahsin Iqbal +2
    Molecules (Basel, Switzerland)Peer-reviewedCC BYFull textGeneral chemistryEurope PMC
  2. PolyGLOT: Graph-Token Language Modeling for Unified Polymer Property Prediction and Programmable Generation
    Tianle Yue, Ying Li
    ChemRxivPreprintCC BY-NC-NDFull textMaterials & polymersChemRxiv
  3. PolyBench: A Reproducible Open Benchmark Platform for Repeat-Unit-Based Polymer Property Prediction
    Wenzhuo Feng, Lunyang Liu, Hongfei Li
    Polymer science & technology (Washington, D.C.)Peer-reviewedCC BY-NC-NDFull textMaterials & polymersEurope PMCPMC
  4. MesoScreen: A Computational Platform Providing Unified Data for Liquid-Crystal Research
    Keda Yang, Longkun Du, Zirui Lv +8
    MoleculesPeer-reviewedCC BY 4.0Full textMaterials & polymersCrossrefEurope PMC
  5. Materials Property Prediction with a Site-Resolved Three-Dimensional Voxel Convolution Neural Network
    Juo Kim, Kyoungmin Min
    ChemRxivPreprintLicence not statedGeneral chemistryChemRxiv

Cite

BibTeXRIS
BibTeX
@article{zhang2026peptiverse,
  title = {{PeptiVerse: A unified platform for therapeutic peptide property prediction}},
  author = {Yinuo Zhang and Sophia Tang and Tong Chen and Elizabeth Mahood and Sophia Vincoff and Pranam Chatterjee},
  journal = {Nature Communications},
  year = {2026},
  month = jul,
  doi = {10.1038/s41467-026-74167-w},
  url = {https://doi.org/10.1038/s41467-026-74167-w},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - PeptiVerse: A unified platform for therapeutic peptide property prediction
AU  - Yinuo Zhang
AU  - Sophia Tang
AU  - Tong Chen
AU  - Elizabeth Mahood
AU  - Sophia Vincoff
AU  - Pranam Chatterjee
PY  - 2026
DA  - 2026/07/16
JO  - Nature Communications
DO  - 10.1038/s41467-026-74167-w
UR  - https://doi.org/10.1038/s41467-026-74167-w
AB  - 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 pep-tide developability analysis. By unifying property prediction across representations, PeptiVerse directly supports early-stage peptide therapeutic development campaigns and property-aware generative design workflows.
SN  - 2041-1723
ER  -