{
  "id": 6903,
  "url": "https://arxiv.org/abs/2603.21108v1",
  "title": "DMMRL: Disentangled Multi-Modal Representation Learning via Variational Autoencoders for Molecular Property Prediction",
  "summary": "Molecular property prediction constitutes a cornerstone of drug discovery and materials science, necessitating models capable of disentangling complex structure-property relationships across diverse molecular modalities. Existing approaches frequently exhibit entangled representations--conflating structural, chemical, and functional factors--thereby limiting interpretability and transferability. Furthermore, conventional methods inadequately exploit complementary information from graphs, sequenc",
  "authors": "Long Xu, Junping Guo, Jianbo Zhao, Jianbo Lu, Yuzhong Peng",
  "category": "research",
  "topics": "safety-alignment,healthcare,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-22T07:53:08.000Z",
  "fetched_at": "2026-07-14T16:32:50.145Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6903",
  "original_url": "https://arxiv.org/abs/2603.21108v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}