Molecular Representations in Deep-Learning Models for Chemical Property Prediction-Reference-Cited by-同舟云学术

Molecular Representations in Deep-Learning Models for Chemical Property Prediction

Published:2022 Issue: Volume: Page:1591-1596
ISSN:1570-7946
Container-title:Computer Aided Chemical Engineering
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Author:

Aouichaoui Adem R.N.,Fan Fan,Mansouri Seyed Soheil,Sin Jens Abildskov Gürkan

Publisher

Elsevier

Reference11 articles.

1. ‘Comparison of Group-Contribution and Machine Learning-based Property Prediction Models with Uncertainty Quantification’, in Computer Aided Chemical Engineering;Aouichaoui;Elsevier Masson SAS,2021

2. MOBOpt — multi-objective Bayesian optimization;Galuzio;SoftwareX,2020

3. ‘Neural Message Passing for Quantum Chemistry’, 34th International Conference on Machine Learning;Gilmer;ICML,2017

4. A method to estimate the enthalpy of formation of organic compounds with chemical accuracy;Hukkerikar;Fluid Phase Equilibria,2013

5. Group-contribution + (GC +) based estimation of properties of pure components: Improved property estimation and uncertainty analysis;Hukkerikar;Fluid Phase Equilibria,2012

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1. Data-driven structural synthesis of supercritical CO2 power cycles;Frontiers in Chemical Engineering;2023-10-16

2. Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models;Journal of Chemical Information and Modeling;2023-01-30

3. Group Contribution Revisited: The Enthalpy of Formation of Organic Compounds with “Chemical Accuracy” Part III;AppliedChem;2022-11-11