Molecular partition coefficient from machine learning with polarization and entropy embedded atom-centered symmetry functions-Reference-Cited by-同舟云学术

Molecular partition coefficient from machine learning with polarization and entropy embedded atom-centered symmetry functions

Published:2022 Issue:38 Volume:24 Page:23082-23088
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Zhu Qiang¹^ORCID,Jia Qingqing¹,Liu Ziteng¹,Ge Yang¹,Gu Xu¹,Cui Ziyi¹,Fan Mengting¹,Ma Jing¹^ORCID

Affiliation:

1. Key Laboratory of Mesoscopic Chemistry of Ministry of Education Institute of Theoretical and Computational Chemistry School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, P. R. China

Abstract

Efficient prediction of the partition coefficient (log P) between polar and non-polar phases could shorten the cycle of drug and materials design.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CP/D2CP02648A

Reference42 articles.

1. The evolution and future of environmental partition coefficients

2. ADMET in silico modelling: towards prediction paradise?

3. Less may be more: an informed reflection on molecular descriptors for drug design and discovery

4. Prediction of Drug Solubility by the General Solubility Equation (GSE)

5. Solubility and partitioning I: Solubility of nonelectrolytes in water

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