{
  "abstract": "Shape-based virtual screening methods are widely used in drug discovery. Alignment-dependent approaches often provide high screening accuracy but are computationally expensive, whereas alignment-free methods are faster but generally less accurate. Here, we present GE-MolSG, a novel alignment-free molecular descriptor that combines shape and electrostatic information using spectral graph representations derived from the graph Laplacian. GE-MolSG outperforms the established alignment-free ElectroShape method and achieves performance comparable to ESP-Sim without requiring molecular alignment. To demonstrate the extensibility of the graph-based framework, we further explore a proof-of-concept patch-based extension termed GM-MolSG, which incorporates additional surface features. The graph-based formulation provides a flexible and extensible framework for incorporating surface-associated molecular properties into compact descriptors for rapid alignment-free similarity searching.\n\nScientific contribution\n\nGE-MolSG introduces a novel alignment-free spectral graph approach for molecular comparison that combines shape and electrostatic information within compact spectral descriptors. The method outperforms the established alignment-free ElectroShape descriptor and achieves performance comparable to ESP-Sim without requiring molecular alignment, while providing a flexible basis for incorporating additional surface-associated molecular properties. As a proof-of-concept demonstration of this extensibility, we further explore a patch-based extension, GM-MolSG, incorporating local geometric and chemical surface descriptors.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "James Middleton"
    },
    {
      "name": "Gian Marco Ghiandoni"
    },
    {
      "name": "Martin J. Packer"
    },
    {
      "name": "Mengdie Zhuang"
    },
    {
      "name": "Valerie J. Gillet"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bxbhyeq92c3bj.bib",
  "cited_by": 0,
  "doi": "10.1186/s13321-026-01310-6",
  "fulltext_url": null,
  "id": "bxbhyeq92c3bj",
  "issns": [
    "1758-2946"
  ],
  "landing_url": "https://link.springer.com/article/10.1186/s13321-026-01310-6",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7223046077",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/bxbhyeq92c3bj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:48Z",
      "native_id": "10.1186/s13321-026-01310-6",
      "source": "crossref",
      "url": "https://link.springer.com/article/10.1186/s13321-026-01310-6"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "issn:1758-2946",
      "subfield": "theoretical"
    }
  ],
  "title": "Graph Laplacian spectral descriptors for alignment-free molecular shape and electrostatic comparison",
  "title_text": "Graph Laplacian spectral descriptors for alignment-free molecular shape and electrostatic comparison",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:48Z",
  "url": "https://research.draconitic.com/paper/bxbhyeq92c3bj",
  "venue": "Journal of Cheminformatics",
  "versions": []
}