arXiv Machine Learning
PeptSol: A Translational Benchmark for Protected-Peptide Solubility in Drug Manufacturing
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arXiv AI
Oct 2
Pepti-drift: Scalable Safe-Active Peptide Generation Without Inference-Time Guidance
Pepti-drift is a one‑step peptide generation framework that uses a single latent refinement and parallel decoding, avoiding costly inference‑time guidance. It incorporates attraction toward target‑specific binders and repulsion from liability‑associated regions, achieving an 18.37% predicted Safe‑Active yield across 88 held‑out targets. The method delivers competitive gains to multi‑property‑guided baselines while reducing generation cost by 468×, enabling scalable and fair high‑throughput peptide design.
arXiv AI
Oct 31
PepEVOLVE: Position-Aware Dynamic Peptide Optimization via Group-Relative Advantage
PepEVOLVE is a dynamic, position‑aware framework for optimizing macrocyclic peptides that learns where to edit and how to improve multiple properties. It enhances pretraining with dynamic masking and CHUCKLES shifting, employs a multi‑armed‑bandit router to identify high‑reward residues, and uses an evolving optimization algorithm with group‑relative advantage to stabilize updates. In in‑silico tests on a Rev‑binding macrocycle benchmark, PepEVOLVE surpassed PepINVENT, achieving higher permeability‑weighted scores and converging faster while respecting structural constraints.
arXiv Machine Learning
Oct 21
Ensemble-Aware Structural Encoding (EASE): Conditioning Protein Encoders on Peptide Conformational Ensembles
Ensemble-Aware Structural Encoding (EASE) is a new framework that conditions protein encoders on peptide conformational ensembles rather than single sequences or structures. By selecting central conformers that capture a peptide’s major structural states and fusing their representations with a permutation‑invariant operator, EASE produces a peptide‑level representation that better reflects conformational variability. In evaluations on six peptide property‑prediction tasks, EASE consistently outperforms single‑structure baselines, achieving an average absolute gain of about 3.0% and improving further as more conformers are incorporated.
arXiv AI
Aug 25
PepLLM: ESM-Guided Llama for Structured Protein-Peptide Binding Interface Analysis
arXiv:2608.21367v1 Announce Type: cross Abstract: Protein-peptide interactions are central to cellular regulation and peptide-based drug discovery, yet existing computational methods mainly focus on...
arXiv AI
Sep 10
Partner-aware Peptide-Protein Interaction Prediction and Target-conditioned Peptide Generation
arXiv:2604.18467v3 Announce Type: replace-cross Abstract: Motivation: Peptide-protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterizat...
Hugging Face Trending Papers
Oct 20
Ensemble-Aware Structural Encoding (EASE): Conditioning Protein Encoders on Peptide Conformational Ensembles
Protein foundation models provide transferable representations for downstream peptide prediction tasks. However, existing representations typically reduce a peptide to a single sequence or structure,...