arXiv AI
PepLLM: ESM-Guided Llama for Structured Protein-Peptide Binding Interface Analysis
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arXiv AI
Sep 3
ProbeMatchDTI: Probe-Driven Multi-Scale Biochemical Pattern Matching for Drug-Target Interaction Prediction
ProbeMatchDTI is a new framework for drug‑target interaction prediction that uses probe‑driven pattern matching to preserve weak biochemical signals. It introduces IterProbe, which retains contextual states across refinement depths and selects them with learnable probes, and BindingProbe, which models drug‑protein complementarity at both local and whole‑pair levels. Experiments show that ProbeMatchDTI outperforms existing methods, improving AUC‑ROC by 2.0% on BindingDB and 0.5% on DrugBank, and its predictions can be integrated into downstream drug‑discovery workflows.
arXiv AI
3d ago
HierDTA: Hierarchical Structure-Aware Representation Learning for Drug--Target Affinity Prediction
HierDTA is a hierarchical structure‑aware framework for drug–target affinity prediction that represents drugs as atom–motif heterogeneous graphs and proteins as residue interaction graphs. It integrates atom and residue representations via cross‑attention, supplemented by molecule fingerprints and pretrained ESM‑2 embeddings, achieving superior performance on the Davis and KIBA datasets and strong generalization in cold‑start scenarios. Ablation studies and interpretability analyses confirm the contributions of its hierarchical augmentation and interaction components, and case studies demonstrate early enrichment of candidate bioactive compounds.
Hugging Face Trending Papers
Sep 2
ProbeMatchDTI: Probe-Driven Multi-Scale Biochemical Pattern Matching for Drug-Target Interaction Prediction
ProbeMatchDTI introduces a probe-driven framework for drug‑target interaction prediction that preserves weak biochemical signals by using IterProbe to retain contextual states and BindingProbe to model cross‑entity complementarity at multiple scales. The method improves AUC‑ROC by 2.0% on BindingDB and 0.5% on DrugBank compared to prior biochemical representation learning approaches. Feature‑level analyses confirm the effectiveness of the probe-driven pattern matching, and the predictions are linked to an evidence‑guided downstream drug‑discovery workflow for candidate refinement and validation planning.
arXiv Machine Learning
3d ago
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 Machine Learning
Oct 8
PIT-GCL: Protein Interaction using Topological Graph Contrastive Learning
arXiv:2610.04850v2 Announce Type: replace Abstract: Protein binding prediction is central to target identification, therapeutic binder design, and large scale screening, yet remains challenging becau...
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.