Leveraging 2D molecular graph pretraining for improved 3D conformer generation with graph neural networks-Reference-Cited by-同舟云学术

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Leveraging 2D molecular graph pretraining for improved 3D conformer generation with graph neural networks

Published:2024-04 Issue: Volume:183 Page:108622
ISSN:0098-1354
Container-title:Computers & Chemical Engineering
language:en
Short-container-title:Computers & Chemical Engineering

Author:

Alhamoud Kumail,Ghunaim Yasir,Alshehri Abdulelah S.,Li Guohao,Ghanem Bernard,You Fengqi^ORCID

Publisher

Elsevier BV

Reference64 articles.

1. Paradigm shift: the promise of deep learning in molecular systems engineering and design;Alshehri;Front. Chem. Eng.,2021

2. Machine learning for multiscale modeling in computational molecular design;Alshehri;Curr. Opin. Chem. Eng.,2022

3. Deep learning to catalyze inverse molecular design;Alshehri;Chem. Eng. J.,2022

4. Deep learning and knowledge-based methods for computer-aided molecular design—Toward a unified approach: state-of-the-art and future directions;Alshehri;Comput. Chem. Eng.,2020

5. The process of structure-based drug design;Anderson;Chem. Biol.,2003

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1. A critical methodological revisit on group-contribution based property prediction of ionic liquids with machine learning;Chemical Engineering Science;2024-10

2. Generative AI and process systems engineering: The next frontier;Computers & Chemical Engineering;2024-08

3. Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction;Journal of Chemical Information and Modeling;2024-07-15

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