Ourotide: decoding the hierarchical peptide recognition for generative design

REAPS: An All-Atom Receptor-Aware Geometric Deep Learning Framework for De Novo Design of Linear and Macrocyclic Peptide Binders

The REceptor-Aware Peptide Sequence Designer (REAPS), a geometric graph neural network that reframes sequence design for peptide binders by treating the receptor as a fully observable all-atom context, is introduced.

LGBind: A structure-guided deep learning framework for accurate prediction of protein-ligand binding sites.

LGBind is introduced, a structure-guided deep learning framework for generalized, ligand-conditioned binding site identification and demonstrates profound robustness and generalization in predicting novel ligand-target interactions, and demonstrates remarkable versatility.

AtomWeaver: Multi-Component Flow Matching with a Structured Geometric Prior Facilitates Non-Canonical Peptide Design

Fixed-backbone sequence discovery, or inverse folding, is a critical recurring task in the development of new polypeptide therapeutics. Once promising backbones are established for a target pocket, computational inverse folding methods greatly help accelerate generation of candidate sequences. Such methods are mature f...

PepSpace: An Automated, Physics-Driven Directed Evolution Platform for the De Novo Design of Specific Peptide Binders and Condensate Modulators

Intrinsically disordered proteins, topologically complex protein surfaces, and biomolecular condensates regulate essential cellular processes but remain difficult to drug with conventional small molecules. Here, we present PepSpace, a physics-driven directed-evolution platform coupling residue-resolution coarse-grained...

OmniScore: Universal Scoring of Diverse Biomolecular Complexes via Equivariant Geometry-Aware Discrete Representation Learning

OmniScore is introduced, a universal structure-based framework that learns a shared geometry-aware representation of complexes once and then adapts it to downstream scoring through lightweight task-specific heads, suggesting that geometry-aware pretraining can provide a reusable scoring backbone for tasks that depend o...

Quantitative and interface-aware prediction of peptide–protein interactions by VITAL