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.
Standard k-mer methods treat amino acids as categorical tokens without directly encoding physicochemical properties. Although physicochemical properties have been incorporated into various bioinformatics tasks, their potential as a direct, systematic alternative for the k-mer counting paradigm has not been fully evaluated. We present PhysioChem-K-mer, a framework that transforms protein sequences into physicochemical property-based feature spaces, serving as an alternative to conventional amino-acid-identity k-mer representations. Our main hypothesis is that property-based representations capture functional constraints more effectively than traditional amino acid-based methods. To test this hypothesis, we created a controlled benchmark comprising 1500 synthetic sequences spanning 10 diverse protein families. The dataset retained core functional motifs while deliberately excluding evolutionary patterns typically found in natural biological sequences. Notably, our hydropathy-based PhysioChem-K-mer achieved a classification accuracy of 81.33% on a controlled synthetic benchmark, representing an absolute gain of 44.33% points over standard 3-mer methods (37.00%). The framework was further evaluated using real UniProt/Swiss-Prot data, comprising 11,620 sequences across 10 families, to ensure practical generalizability. Based on real data, PhysioChem-Hydropathy achieved 64.63%, an absolute gain of 47.68% points over the standard 3-mer baseline (16.95%), while reducing features by 73.9% and training time by 81.6%. By directly integrating biochemical knowledge into feature representations as a primary design principle, PhysioChem-K-mer combines interpretability with computational efficiency. These results suggest that physicochemical properties offer a vital source of information for protein classification, 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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