Treat Molecular Linear Notations as Sentences: Accurate Quantitative Structure–Property Relationship Modeling via a Natural Language Processing Approach-Reference-Cited by-同舟云学术

Treat Molecular Linear Notations as Sentences: Accurate Quantitative Structure–Property Relationship Modeling via a Natural Language Processing Approach

Published:2023-03-08 Issue:12 Volume:62 Page:5336-5346
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Zhou Zhengtao¹,Eden Mario²^ORCID,Shen Weifeng¹^ORCID

Affiliation:

1. School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China

2. Department of Chemical Engineering, Auburn University, Auburn, Alabama 36849, United States

Funder

Central University Basic Research Fund of China

National Natural Science Foundation of China

National Natural Science Foundation for Excellent Young Scientists of China

Chongqing Joint Chinese Medicine Scientific Research Project

Chongqing Science Fund for Distinguished Young Scholars

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.2c04070

Reference64 articles.

1. Deep Learning in Chemistry

2. Deep learning for computational chemistry

3. Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks

4. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

5. Generative Recurrent Networks for De Novo Drug Design

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