Application of interpretable group-embedded graph neural networks for pure compound properties

Author:

Aouichaoui Adem R.N.ORCID,Fan Fan,Abildskov Jens,Sin Gürkan

Publisher

Elsevier BV

Subject

Computer Science Applications,General Chemical Engineering

Reference96 articles.

1. Next generation pure component property estimation models: with and without machine learning techniques;Alshehri;AlChE J.,2022

2. Comparison of group-contribution and machine learning-based property prediction models with uncertainty quantification;Aouichaoui,2021

3. Combining group-contribution concept and graph neural networks toward interpretable molecular property models;Aouichaoui;J. Chem. Inf. Model.,2023

4. Uncertainty estimation in deep learning-based property models: graph neural networks applied to the critical properties;Aouichaoui;AlChE J.,2022

5. Application of outlier treatment towards improved property prediction models;Aouichaoui,2022

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