Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN)-Reference-Cited by-同舟云学术

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Solubility Prediction from Molecular Properties and Analytical Data Using an In-phase Deep Neural Network (Ip-DNN)

Published:2021-05-17 Issue:22 Volume:6 Page:14278-14287
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Kurotani Atsushi¹,Kakiuchi Toshifumi²,Kikuchi Jun¹³⁴^ORCID

Affiliation:

1. RIKEN Center for Sustainable Resource Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

2. AGC Yokohama Technical Center, 1-1 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

3. Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

4. Graduate School of Bioagricultural Sciences, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya, Aichi 464-0810, Japan

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.1c01035

Reference70 articles.

1. Solutions of Nonelectrolytes

2. 50 Years with solubility parameters—past and future

3. Application of Hansen Solubility Parameters to predict drug–nail interactions, which can assist the design of nail medicines

4. Measurements of the Hansen solubility parameters of mites and cockroaches to improve pest control applications

5. Optimization of Subcritical Fluid Extraction of Bioactive Compounds Using Hansen Solubility Parameters

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