Graph Laplacian spectral descriptors for alignment-free molecular shape and electrostatic comparison
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.
Scientific contribution
GE-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.
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BibTeX
@article{middleton2026graph,
title = {{Graph Laplacian spectral descriptors for alignment-free molecular shape and electrostatic comparison}},
author = {James Middleton and Gian Marco Ghiandoni and Martin J. Packer and Mengdie Zhuang and Valerie J. Gillet},
journal = {Journal of Cheminformatics},
year = {2026},
month = oct,
doi = {10.1186/s13321-026-01310-6},
url = {https://link.springer.com/article/10.1186/s13321-026-01310-6},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Graph Laplacian spectral descriptors for alignment-free molecular shape and electrostatic comparison
AU - James Middleton
AU - Gian Marco Ghiandoni
AU - Martin J. Packer
AU - Mengdie Zhuang
AU - Valerie J. Gillet
PY - 2026
DA - 2026/10/20
JO - Journal of Cheminformatics
DO - 10.1186/s13321-026-01310-6
UR - https://link.springer.com/article/10.1186/s13321-026-01310-6
AB - 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. Scientific contribution GE-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.
SN - 1758-2946
ER -