Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts-Reference-Cited by-同舟云学术

Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts

Published:2021-10-05 Issue:41 Volume:6 Page:27578-27586
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Ebi Tomoya¹,Sen Abhijit²,Dhital Raghu N.²,Yamada Yoichi M. A.²^ORCID,Kaneko Hiromasa¹²^ORCID

Affiliation:

1. Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan

2. RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan

Funder

Japan Society for the Promotion of Science

Informatics Data Science Promotion Program from RIKEN CSRS

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.1c04826

Reference35 articles.

1. Best Practices for QSAR Model Development, Validation, and Exploitation

2. How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)

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4. QSPR prediction of physico-chemical properties for REACH

5. PLS-regression: a basic tool of chemometrics

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