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Transferring Peptide-Target Interaction Patterns to Small Molecules by Joint Guidance for Diffusion and Flow Matching — arXiv:2610.41322

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41322
License (as archived): CC0-1.0
Archived: 2026-11-02T07:40:39.052Z
SHA-256 of the archived text: 2c7687f00fa626cca7d012b96fa50e6903752d66a69a1b96ba83109938dc9606
Stable URL of this page: https://metraipolis.com/documento/dc_daacc5aebd43

How to cite

Transferring Peptide-Target Interaction Patterns to Small Molecules by Joint Guidance for Diffusion and Flow Matching — arXiv:2610.41322 — arXiv (en), https://arxiv.org/abs/2610.41322 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_daacc5aebd43 (Metraipolis archive dc_daacc5aebd43, sha256 2c7687f00fa626cca7d012b96fa50e6903752d66a69a1b96ba83109938dc9606)

BibTeX

@misc{dcdaacc5aebd43,
  title = {Transferring Peptide-Target Interaction Patterns to Small Molecules by Joint Guidance for Diffusion and Flow Matching — arXiv:2610.41322},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41322},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_daacc5aebd43; sha256 2c7687f00fa626cca7d012b96fa50e6903752d66a69a1b96ba83109938dc9606},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41322. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generating small molecules to target protein-protein interactions (PPIs) remains one of the most challenging yet high-reward frontiers in drug discovery. Unlike traditional small-molecule drugs that bind to deep, well-defined pockets, PPI interfaces often comprise broad, shallow binding surfaces. A natural strategy is to leverage known peptide-target interactions, as peptide ligands provide rich interface-level constraints. However, a major algorithmic challenge is that rigid atom-level conditioning on peptide structures can over-constrain the generative space and exclude valid small-molecule solutions. To address this challenge, we present PIFT (Peptide InterFace Transfer), a novel framework that transfers peptide-target interaction patterns to small molecules through joint sampling-time guidance of masked discrete diffusion and continuous flow matching. PIFT first maps peptide-target and small-molecule-target interfaces into a shared, target-conditioned interaction space, capturing transferable interface information across molecular modalities. The resulting peptide-derived reference then jointly steers building-block logits and 3D coordinates toward peptide-compatible molecules while preserving a strong adapted molecular prior. Extensive experiments across diverse PPI targets show that the learned representation captures cross-modal interaction compatibility, while peptide guidance improves interface recovery and achieves strong docking performance and favorable molecular properties.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41322 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T07:37:00.572Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).