PeptiVerse is a unified platform that leverages large foundation models to predict diverse peptide developability properties from both amino acid sequences and SMILES representations, enabling accessible, scalable analysis for peptide drug design.
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. Therapeutic peptides are an increasingly important drug modality, combining the flexibility of small molecules with the specificity and low immunogenicity of antibodies, but their development requires simultaneous optimization of multiple physicochemical and pharmacological properties. Here, the authors present PeptiVerse, a unified platform that leverages large foundation models to predict diverse peptide developability properties from both amino acid sequences and SMILES representations, enabling accessible, scalable analysis for peptide drug design.
Structure-Aware Multi-Label Therapeutic Peptide Predictor (SA-MTP), a structure-aware framework designed for multifunctional annotation of therapeutic peptides, is introduced.
Wenping Yu, Zhewen Li, Wei Xu et al.· Briefings in Bioinformatics· 0 citations
This review systematically examines the key methodological innovations, including peptide representation learning, multi-modal fusion strategies, multi-label learning paradigms, and emerging predictive frameworks empowered by deep neural architectures and ProtLM-based embeddings, and summarizes the practical applications of these models in peptide database mining, functional mechanism interpretation, and mutation effect prediction.
Accurate prediction of peptide structures and peptide-receptor complexes is essential for rational peptide drug development. However, the inherent conformational flexibility of short and disordered peptides presents a fundamental challenge. The AlphaFold model series, which has progressed from AlphaFold2 through AlphaFold-Multimer to AlphaFold3, has substantially advanced computational peptide structure prediction through innovations in geometric reasoning (invariant point attention) and interface-focused confidence metrics (ipTM score), achieving high accuracy for both monomeric peptide structures and multi-chain complexes. However, these models output static conformations, whereas many bioactive peptides adopt their functional conformations only upon binding-often corresponding to low-probability states that static predictions may overlook, leading to failures in virtual screening. This review synthesizes recent advances in the AlphaFold series for peptide studies and applications, discusses their current strengths in structure prediction and receptor-binding analysis, and examines the limitations in capturing conformational dynamics, transient interactions, and chemical modifications. Recent studies have suggested that integrated computational strategies that combine AlphaFold predictions with molecular dynamics simulations, free energy calculations, and ensemble sampling to enhance predictive accuracy and better represent the dynamic nature of peptide-drug interactions. These complementary approaches position AlphaFold as a central computational platform in structure-guided peptide drug design, enabling more efficient lead identification and optimization while bridging the gap between static computational predictions and the complex biophysical reality of peptide therapeutics.
These findings provide practical guidance for integrating open-source protein structure prediction models into AI-driven nanobody discovery pipelines while highlighting the need for improved generalization across antigens.
Yannick Vogt, Rebekka Roßberg, Jan Habermann et al.· Frontiers in Bioinformatics· 0 citations
A novel protein segment capture strategy for drug-target affinity prediction (SAPDTA), which is designed to extract local protein features through a local block capture approach, enabling more flexible extraction of protein structure information at different levels.
Zihao Fang, Guanqiu Qi, Stanley Tang et al.· Sensors and AI· 0 citations
MultiMol is a multimodal framework that integrates SMILES sequences, molecular images, molecular graphs, and 3D conformations through tailored pre-training tasks and a scalable fusion mechanism, and consistently outperforms existing methods in cyclic peptide permeability prediction.
Haowen Chen, Xiuxiu Chao, Weihao Ou et al.· IEEE journal of biomedical a...· 0 citations