|
Affiliation |
Faculty of Science Depertment of Chemistry |
|
Title |
Professor |
|
External Link |
|
TERAMAE Hiroyuki
|
|
|
Research Areas 【 display / non-display 】
-
Nanotechnology/Materials / Fundamental physical chemistry
From School 【 display / non-display 】
-
Kyoto University Faculty of Engineering Graduated
- 1979.03
Country：Japan
From Graduate School 【 display / non-display 】
-
Kyoto University Graduate School, Division of Engineering Doctor's Course Completed
- 1984.03
Country：Japan
-
Kyoto University Graduate School, Division of Engineering Master's Course Completed
- 1981.03
Country：Japan
Employment Record in Research 【 display / non-display 】
-
Josai University Faculty of Science Depertment of Chemistry Professor
2004.04
External Career 【 display / non-display 】
-
Meiji University Lecturer
2009.04 - 2023.03
Country：Japan
-
Rikkyo University Lecturer
2002.04 - 2006.03
-
ATR適応コミュニケーション研究所 主任研究員
2001.10 - 2002.03
-
ATR環境適応通信研究所 主任研究員
1999.01 - 2001.09
-
Rikkyo University Lecturer
1997.04 - 1998.03
Professional Memberships 【 display / non-display 】
-
日本コンピュータ化学会
1998.04
-
アメリカ化学会
1996.04
-
応用物理学会
1992.07 - 1996.03
-
日本化学会
1990.04
Research Career 【 display / non-display 】
-
分子軌道法と機械学習による分子物性の予測
The Other Research Programs
Project Year： 2017.04 -
-
分子軌道法を用いたナノマテリアル内での化学反応に関する研究
Funded Research
Project Year： 2008.12 - 2014.03
-
分子内プロトン移動反応に関する理論的研究
Cooperative Research
Project Year： 2008.04 - 2013.03
-
高次元アルゴリズムによる分子構造最適化の研究
The Other Research Programs
Project Year： 2004.04 -
Papers 【 display / non-display 】
-
Prediction of log P Parameter Using Molecular Orbital Energies and Machine Learning Invited Reviewed
Hiroyuki Teramae
22 ( 2 ) 34 - 36 2024.02
Authorship：Lead author, Corresponding author Language：Japanese Publishing type：Research paper (scientific journal)
-
Prediction of Entropy by Machine Learning with Molecular Orbital Energies Invited Reviewed
Takafumi Yuuki, Wakana Nakahara, Hiroyuki Teramae
22 ( 2 ) 31 - 33 2024.02
Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal)
-
Machine Learning Study of Antioxidant Effects with Molecular Orbital Energies as Explanatory Variables Invited Reviewed
Journal of Computational Chemistry, Japan 21 ( 4 ) 103 - 105 2023.04
Authorship：Lead author, Corresponding author Language：Japanese Publishing type：Research paper (scientific journal)
-
Prediction of molecular properties with machine learning and molecular orbital energies Invited Reviewed
Hiroyuki Teramae, Meiyan Xuan, Jun Takayama, Mari Okazaki and Takeshi Sakamoto
AIP Conference Proceedints 2611 02007 2022.11
Authorship：Lead author, Corresponding author Language：English Publishing type：Research paper (international conference proceedings)
-
Possible Prediction of Molecular Properties with Machine Learning and Molecular Orbital Energies Invited Reviewed
Hiroyuki Teramae, Xuan Meiyan, Tsukasa Yamashita, Jun Takayama, Mari Okazaki, Takeshi Sakamoto
Proceedings of International Symposium on Environmental-Life Science and Nanoscales Technology 2019 XVII - XXI 2020.09
Authorship：Lead author Language：English Publishing type：Research paper (international conference proceedings) Publisher：University of Yangon
The ferulic acid is known to have strong antioxidant properties. In the present study, we have investigated the electronic structures of the ferulic acid and its radical extracting the hydrogen atom from its phenolic hydroxyl group. We have discussed the relation of the results with the radical scavenging activity with the DPPH reagent, IC50, measured by Sakamoto et al. by several machine learning models.
We use Gaussian16 program package to calculate the optimized geometries and the molecular orbitals of FA and its derivatives at RHF/6-31G** level and the radicals of FA and its derivatives which are made by removing the hydrogen atom from the phenolic hydroxyl group. The machine learning is performed with the R/caret packages.
We use the orbital energy levels of the radical forms of SOMO, SOMO-1, SOMO, LUMO, and LUMO, the neutral forms of HOMO-1, HOMO, LUMO, and LUMO+1, and the energy difference between the radical and neutral forms as the explanatory variables. We make the machine learning with these ten explanatory variables and IC50 value as the explained variable. For the regression method, we use partial least square, random forest, neural network, and krlsRadial.
All the methods give moderate/strong correlation coefficients and there should be a strong correlation. Furthermore, when we examine the machine learning with only the orbital energy levels of the radical forms, the correlation coefficients are almost the same.
In conclusion, we confirm the IC50 values of the ferulic acid can be predicted by just molecular orbital energies
Books and Other Publications 【 display / non-display 】
-
ケモインフォマティクスにおける データ収集の最適化と解析手法
寺前裕之（ Role： Contributor , 第4章第1節ケモインフォマティクスにおける機械学習モデルの種類と具体的活用法）
技術情報協会 2023.04 （ ISBN:978-4-86104-944-6 ）
Total pages：657 Responsible for pages：209-217 Language：Japanese Book type：Scholarly book
-
Materials Informatics Questions and Answers
Masahiro Kaneko, Kimito Funatsu, Hiroyuki Teramae etc.（ Role： Joint author , Chapter 8, Section 3, Question 4）
JOHOKIKO CO. LTD. 2020.12 （ ISBN:978-4-86502-204-9 ）
Total pages：597 Responsible for pages：516-518 Language：Japanese Book type：Scholarly book
A number of questions that arise during the introduction and operation of materials informatics are specifically resolved in a Q & A format.
-
導電性材料をめぐる最近の動向
寺前裕之（ Role： Sole author）
材料技術研究協会 1992.04
Language：Japanese Book type：Scholarly book
-
ポリアセチレンの電子構造
山邊時雄，寺前裕之（ Role： Sole author , 主要部分の執筆）
化学同人 1985.04
Language：Japanese Book type：Scholarly book
Misc 【 display / non-display 】
-
Relation Between Machine Learning and Chemistry Invited
2022.03
Language：Japanese
-
PubChem のデータを用いた分子座標の作成‐データベースを用いてGaussian16 の入力ファイルを作成する方法 ‐
寺前裕之
城西情報科学研究 29 15 - 26 2022.03
Authorship：Lead author, Last author Language：Japanese Publishing type：Rapid communication, short report, research note, etc. (bulletin of university, research institution)
-
計算化学汎用プログラム 分子設計統合ソフト HyperChem
寺前裕之
PETROTECH 30 ( 5 ) 346 - 350 2007.01
Language：Japanese Publishing type：Article, review, commentary, editorial, etc. (international conference proceedings) Publisher：（石油学会）
Presentations 【 display / non-display 】
-
分子軌道エネルギーによる構造活性相関法の開発
寺前 裕之, 藤堂 浩明
日本コンピュータ化学会2023年秋季年会 2023.11 日本コンピュータ化学会
Event date： 2023.11
Language：Japanese Presentation type：Oral presentation (general)
Venue：東京 Country：Japan
-
機械学習による分子軌道エネルギーのみを説明変数としたエントロピーの予測
結城敬史、寺前裕之
日本コンピュータ化学会2023年秋季年会 2023.11 日本コンピュータ化学会
Event date： 2023.11
Language：Japanese Presentation type：Poster presentation
Venue：東京 Country：Japan
-
分子軌道エネルギーの機械学習による構造活性相関
寺前 裕之, 藤堂 浩明
第46回ケモインフォマティクス討論会 2023.11 日本化学会ケモインフォマティクス部会
Event date： 2023.11
Language：Japanese Presentation type：Oral presentation (general)
Venue：東京 Country：Japan
-
分子軌道エネルギーを用いた機械学習によるlogPの予測
寺前裕之
分子科学討論会2023 2023.09
Event date： 2023.09
Language：Japanese Presentation type：Oral presentation (general)
Venue：大阪 Country：Japan
-
分子軌道エネルギーを用いた機械学習によるlogPの予測
寺前裕之
日本コンピュータ化学会2023年春季年会 2023.06 日本コンピュータ化学会
Event date： 2023.06
Language：Japanese Presentation type：Oral presentation (general)
Venue：東京 Country：Japan
Other research activities 【 display / non-display 】
-
Editorial board of Journal of Chemistry
2015.01 - 2018.08