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January 1, 2025Chemical ScienceOpen Access

A Fragment Based Approach Towards Curating, Comparing and Developing Machine Learning Models Applied in Photochemistry

Raúl Pérez‐Soto Mihai V. Popescu Sabari Kumar Leticia Adao Gomes Changyeob Lee Elijah Shore Steven A. Lopez Robert S. Paton Seonah Kim

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Key Points

The study improves the prediction of molecular properties using advanced machine learning techniques.
Graph neural networks significantly correlate atomic descriptors with molecular outcomes, enhancing model accuracy.
The approach emphasizes a fragment-based method for curating and comparing various models in photochemistry.
This research may foster new capabilities in predictive modeling for chemical applications.

Implication

This approach develops machine learning models for predicting molecular properties, highlighting the role of graph neural networks in photochemistry.

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Cite This Study

Pérez‐Soto et al. (2025) studied this question.

synapsesocial.com/papers/68f0ba59c50c73ebef9fa9e8 https://doi.org/10.1039/d5sc05615b

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1A Fragment Based Approach Towards Curating, Comparing and Developing Machine Learning Models Applied in Photochemistry2025
2Linear Graphlet Models for Accurate and Interpretable Cheminformatics2024 · 2 citations
3Linear Graphlet Models for Accurate and Interpretable Cheminformatics2024
4FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of InterpretabilityOriginal Research · 2026 · 8 citations
5A New Fingerprint and Graph Hybrid Neural Network for Predicting Molecular Properties2024 · 22 citations

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