{
  "id": 16582,
  "url": "https://arxiv.org/abs/2608.02688v1",
  "title": "Learning Molecular Representations from Cellular Phenotypes with Structure Preservation",
  "summary": "Phenotypic drug discovery enables the discovery of functional relationships between molecular structures and cellular responses. However, existing multimodal representation learning methods often optimize cross-modal alignment without considering the intrinsic organization of chemical space, resulting in distorted molecular representations and loss of structural information. We propose \\textbf{PhenMol}, a structure-preserving framework for phenotype-aware molecular representation learning. PhenM",
  "authors": "Xuan Lin, Jingyu Sheng, Tengfei Ma, Li Sun, Dapeng Xiong",
  "category": "research",
  "topics": "safety-alignment,healthcare,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T08:33:54.000Z",
  "fetched_at": "2026-08-05T05:10:44.550Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16582",
  "original_url": "https://arxiv.org/abs/2608.02688v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}