PeptiVerse: A Unified Platform for Therapeutic Peptide Property Prediction

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

Keywords: peptide, benchmark, dataset, platform, easy-access

TL;DR: We developed PeptiVerse, a user-friendly platform for peptide developability property prediction.

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.

Presenter: ~Yinuo_Zhang3

Format: Yes, the presenting author will definitely attend in person because they attending ICLR for other complementary reasons.

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Submission Number: 13