Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms-Reference-Cited by-同舟云学术

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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms

Published:2024-07 Issue: Volume: Page:
ISSN:2666-9528
Container-title:Green Chemical Engineering
language:en
Short-container-title:Green Chemical Engineering

Author:

Yang Ao^ORCID,Sun Shirui,Qi Lu,Kong Zong Yang,Sunarso Jaka,Shen Weifeng

Funder

Special Fund of Chongqing Key Laboratory

National Aerospace Science Foundation of China

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

Tianjin Postdoctoral Science Foundation

National Natural Science Foundation of China-Yunnan Joint Fund

Publisher

Elsevier BV

Reference42 articles.

1. Sustainability science;Kates;Science,2001

2. Environmental assessment of chemicals: methods and application to a case study of organic solvents;Hellweg;Green Chem.,2004

3. Greening industrial applications with magnetic-based deep eutectic solvents: a promising future;Zainal-Abidin;J. Ind. Eng. Chem.,2023

4. Chapter 6–Octanol-water partition constant;Amézqueta,2020

5. Assessing the accuracy of octanol–water partition coefficient predictions in the SAMPL6 Part II log P Challenge;Işık;J. Comput. Aided Mol. Des.,2020

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