arXiv Machine Learning
Benchmarking Peptide-Protein Affinity Prediction Across Peptide and Target Shifts
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AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking
arXiv:2607. 05846v1 Announce Type: cross Abstract: Accurate ranking of antibody candidates according to their binding affinity is essential for therapeutic antibody discovery.
arXiv Machine Learning
Oct 5
Beyond Random Splits: Evaluating Drug-Target Affinity Models Under Chemically and Biologically Motivated Distribution Shifts Copy
The study investigates how different distribution shifts affect the evaluation of drug‑target affinity (DTA) models, using 718,800 drug‑protein pairs from ChEMBL and BindingDB. It compares a baseline Morgan‑fingerprint + protein‑CNN model with 12 architectures that combine four drug representations and three ESM‑2 interaction modes, finding that mean validation RMSE rises significantly under protein‑cluster and dual out‑of‑distribution (OOD) scenarios. Model rankings remain consistent across chemical shifts but diverge under protein and dual OOD, indicating that architecture selection depends on the specific extrapolation context rather than just average error or training‑set size. Why it matters: The results suggest that DTA benchmarks should be designed to reflect the chemical and target shifts expected in real deployment, ensuring that chosen models perform reliably when applied to new chemical series or protein targets.
Hugging Face Trending Papers
Jul 7
AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking
Accurate ranking of antibody candidates according to their binding affinity is essential for therapeutic antibody discovery. However, existing methods treat affinity comparisons independently and ignore the contextual information encoded in other labeled comparisons, limiting their ability to capture antigen-specific binding landscapes.
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...
arXiv Machine Learning
Jun 11
MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry
arXiv:2606. 11868v1 Announce Type: new Abstract: De novo peptide sequencing from tandem mass spectrometry is pivotal in proteomics, enabling identification of novel peptides without reference databases.
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...