An interpretable 3D multi-hierarchical representation-based deep neural network for environmental, health and safety properties prediction of organic solvents-Reference-Cited by-同舟云学术

An interpretable 3D multi-hierarchical representation-based deep neural network for environmental, health and safety properties prediction of organic solvents

Published:2024 Issue:7 Volume:26 Page:4181-4191
ISSN:1463-9262
Container-title:Green Chemistry
language:en
Short-container-title:Green Chem.

Author:

Zhang Jun¹,Wang Qin²,Lei Yang³,Shen Weifeng¹^ORCID

Affiliation:

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

2. School of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China

3. School of Chemistry and Chemical Engineering, Hubei Key Laboratory of Coal Conversion and New Carbon Materials, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China

Abstract

A 3D multi-hierarchical representation-based deep neural network (3D-MrDNN) architecture for prediction of the environmental, health and safety properties of organic solvents.

Funder

National Outstanding Youth Science Fund Project of National Natural Science Foundation of China

National Natural Science Foundation of China

Wuhan University of Science and Technology

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/GC/D3GC04801B

Reference38 articles.

1. Green chemistry: challenges and opportunities

2. Tools and techniques for solvent selection: green solvent selection guides

3. Effective solvent system selection in the recrystallization purification of pharmaceutical products

4. Machine learning‐based atom contribution method for the prediction of surface charge density profiles and solvent design

5. Computer-aided molecular design using the Signature molecular descriptor: Application to solvent selection

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