ArticleACS omega2025
Enhanced Prediction of Absorption and Emission Wavelengths of Organic Compounds through Hybrid Graph Neural Network Architectures.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- A Contrastive-Learning-Based Pre-Training Framework for Optical Property Prediction of Low-Data Rhodamines with Interpretable Multitask Graph Neural Networks.Molecules (Basel, Switzerland) · 2026Article
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Interpretable machine learning integrated with TD-DFT descriptors and SHAP analysis for predicting the maximum absorption wavelength of azo dyes.RSC advances · 2025Article
- Enhanced Prediction of Absorption and Emission Wavelengths of Organic Compounds through Hybrid Graph Neural Network Architectures.ACS omega · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of absorption and emission spectra is a crucial task in chemistry and materials science, traditionally addressed with computational methods, such as density functional theory (DFT). While DFT can deliver with high accuracy, its substantial computational cost and long run times hinder its scalability for large molecular systems. In this study, we present an efficient graph neural network (GNN)-based approach for predicting optical properties. Our method combines molecular graph representations with molecular fingerprints, allowing the model to capture detailed structural and electronic features, as well as solvent effects. This simple yet effective GNN framework achieves
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Registered trials
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