{
  "id": 10560378,
  "title": "Tri-Modality Representation Learning for Molecular Property Prediction",
  "url": "https://urgent.news/2026/09/28/tri-modality-representation-learning-for-molecular-property-prediction",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-28T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.22.753673v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Accurate molecular property prediction requires effective molecular representations that can describe a molecule from multiple complementary perspectives. Existing deep learning approaches typically use SMILES strings, two-dimensional molecular graphs, or three-dimensional conformations as inputs. These representations capture different aspects of molecular information: SMILES encodes a…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}