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RangeFinder: Predicting Ligand Property Ranges from Protein Pockets — arXiv:2611.03379

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03379
License (as archived): CC0-1.0
Archived: 2026-11-03T11:23:14.156Z
SHA-256 of the archived text: 8bb5cbe46e782a6de2feacb1f9479a88e2990b31f5115886d1ee47d9f083741e
Stable URL of this page: https://metraipolis.com/documento/dc_2c4d7828ef76

How to cite

RangeFinder: Predicting Ligand Property Ranges from Protein Pockets — arXiv:2611.03379 — arXiv (en), https://arxiv.org/abs/2611.03379 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2c4d7828ef76 (Metraipolis archive dc_2c4d7828ef76, sha256 8bb5cbe46e782a6de2feacb1f9479a88e2990b31f5115886d1ee47d9f083741e)

BibTeX

@misc{dc2c4d7828ef76,
  title = {RangeFinder: Predicting Ligand Property Ranges from Protein Pockets — arXiv:2611.03379},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03379},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2c4d7828ef76; sha256 8bb5cbe46e782a6de2feacb1f9479a88e2990b31f5115886d1ee47d9f083741e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03379. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Identifying ligands for a protein target requires searching a vast chemical space for a comparatively small set of compatible molecules. Existing machine learning approaches primarily address this problem by generating candidate ligands or scoring individual protein-ligand pairs, but provide limited information about the broader chemical space compatible with a binding pocket. We introduce RangeFinder, an SE(3)-equivariant transformer pocket-level model, trained and evaluated on BioLiP2 data, that predicts ranges over nine physicochemical ligand properties directly from pocket structure, chemistry, and evolutionary context. Using an intersection-over-union objective and evaluation metric (range-IoU), RangeFinder achieves a range-IoU of $0.464 \pm 0.002$, outperforming all deployable baselines while requiring only $12.2$ ms per pocket. We also quantify our method's ability to reduce the ligand search space while retaining compatible binders; in this regard, RangeFinder achieves a Reduction-Recall F-Score of $0.543 \pm 0.011$ --- once again outperforming all deployable baselines. Ablation experiments indicate that RangeFinder's performance depends more strongly on the density and quality of ligand supervision than on any individual input modality or specialized training strategy. Together, these results position RangeFinder as an inexpensive and effective molecular filtering prior, with demonstrated potential in narrowing the ligand search space of target pockets before docking, generation, or synthesis. The code for this project can be found at: this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Biomolecules (q-bio.BM).
Pagina del lavoro: https://arxiv.org/abs/2611.03379 — 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 2026-11-03T11:02:37.699Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).