# Partner-aware Peptide-Protein Interaction Prediction and Target-conditioned Peptide Generation

arXiv: https://arxiv.org/abs/2604.18467 · HTML: https://mlsanity.com/paper/2604.18467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [Evaluating Protein Transfer Learning with TAPE](https://mlsanity.com/paper/1906.08230/) (2019)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Interpretable bilinear attention network with domain adaptation improves drug-target prediction](https://mlsanity.com/paper/2208.02194/) (2022)
- [BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions](https://mlsanity.com/paper/2101.12547/) (2021)
- [A general language model for peptide function identification](https://mlsanity.com/paper/2502.15610/) (2025)
- [SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions](https://mlsanity.com/paper/2504.02698/) (2025)
- [GeoPep: A geometry-aware masked language model for protein-peptide binding site prediction](https://mlsanity.com/paper/2510.27040/) (2025)

## Abstract (arXiv)

Motivation: Peptide-protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterization remains too slow for large-scale screening. Existing methods usually emphasize either interaction prediction or peptide generation, leaving candidate prioritization, residue-level interpretation, and target-conditioned expansion insufficiently integrated. Results: We present an integrated framework for early-stage peptide screening that combines a partner-aware prediction and localization model (ConGA-PepPI) with a target-conditioned generative model (TC-PepGen). ConGA-PepPI uses asymmetric encoding, bidirectional cross-attention, and progressive transfer from pair prediction to binding-site localization, while TC-PepGen preserves target information throughout autoregressive decoding via layerwise conditioning. In five-fold cross-validation, ConGA-PepPI achieved 0.839 accuracy and 0.921 AUROC, with binding-site AUPR values of 0.601 on the protein side and 0.950 on the peptide side, and remained competitive on external benchmarks. Under a controlled length-conditioned benchmark, 40.39% of TC-PepGen peptides exceeded native templates in AlphaFold 3 ipTM, and unconstrained generation retained evidence of target-conditioned signal.