{
  "id": 14157,
  "url": "https://arxiv.org/abs/2607.25322v1",
  "title": "From Cellular Responses to Pharmacological Domains: Multimodal Zero-Shot Drug Representation Learning",
  "summary": "Multimodal drug discovery enables drug representation learning beyond chemical structure by incorporating cellular responses such as gene expression and cell morphology. However, direct fusion and instance-level contrastive alignment may mix mechanism-related signals with modality-specific noise and incorrectly separate structurally dissimilar but biologically related compounds. This limitation can obscure transferable mechanism patterns required for predicting the properties of unseen compounds",
  "authors": "Jintao Huang, Lu Leng, Ziyuan Yang",
  "category": "research",
  "topics": "safety-alignment,healthcare,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-28T06:13:25.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/14157",
  "original_url": "https://arxiv.org/abs/2607.25322v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}