TL;DR
The proposed UniPept is a novel model for peptide property prediction that integrates atomic-level and residue-level features to generate robust peptide representations and outperforms competing approaches in interaction and binding affinity prediction tasks.
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.
Vilya-2 is the structure-prediction oracle that de novo peptide design pipelines require--establishing the all-atom approach as a general foundation for the design and evaluation of de novo peptide therapeutics.
PhysioChem-K-mer is presented, a framework that transforms protein sequences into physicochemical property-based feature spaces, serving as an alternative to conventional amino-acid-identity k-mer representations, and is validated here on both synthetic and real-world data.
Vilya-1 is introduced, a deep learning model that addresses two central challenges in macrocycle design: sampling biologically relevant conformations across arbitrary chemistries and predicting key developability properties such as membrane permeability.
HighPlay2 is presented as a feasible framework for the early-stage design and screening of cyclic peptide candidates containing ncAAs, while further affinity maturation and experimental structural validation remain necessary.
HighMorph is presented, an interaction-guided framework that combines protein–protein interaction information with artificial intelligence for rational cyclic peptide design and provides insights for developing therapeutics targeting challenging protein interfaces.
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