Graph neural network architectures for predicting the electrophilicity index: insights from 2D and 3D molecular graph representations

Sandeep Kumar; Annika Bande

doi:10.1039/d6cp00677a

January 1, 2026Physical Chemistry Chemical PhysicsOpen Access

Graph neural network architectures for predicting the electrophilicity index: insights from 2D and 3D molecular graph representations

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Key Points

The aim is to benchmark graph neural networks for predicting the electrophilicity index of QM9 molecules.
Comparison of 2D and 3D graph neural network architectures
Evaluation of geometry-aware models like ALIGNN
Assessment of computational efficiency of 2D models
ALIGNN achieves the highest accuracy in predicting the electrophilicity index
2D models demonstrate superior computational efficiency compared to 3D models

Implication

Benchmarking GNNs shows ALIGNN achieves highest accuracy for predicting electrophilicity index in QM9 molecules, suggesting advanced architecture benefits.

Abstract

Benchmarking 2D and 3D GNNs for predicting the electrophilicity index ( ω ) of QM9 molecules reveals that geometry-aware architectures such as ALIGNN achieve the highest accuracy while 2D models offer superior computational efficiency.

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Cite This Study

Kumar et al. (2026) studied this question.

synapsesocial.com/papers/69fbef68164b5133a91a3552 https://doi.org/10.1039/d6cp00677a

View Full Paper

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Graph neural network architectures for predicting the electrophilicity index: insights from 2D and 3D molecular graph representations

Key Points

Implication

Abstract

Discussion

Cite This Study

Graph neural network architectures for predicting the electrophilicity index: insights from 2D and 3D molecular graph representations

Key Points

Implication

Abstract

Discussion

Cite This Study

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