論文 - 寺前 裕之
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Prediction of pharmacological activity by machine learning using molecular orbital energy as an explanatory variable 招待あり 査読あり
Hiroyuki Teramae, Yuta Miura, Kouichi Shikama, Meiyan Xuan, Jun Takayama, Mari Okazaki and Takeshi Sakamoto
Journal of Computer Chemistry, Japan 23 ( 3 ) 80 - 83 2025年01月
担当区分：筆頭著者, 責任著者 記述言語：日本語 掲載種別：研究論文（学術雑誌）
We constructed a mathematical model to predict the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging capacity (IC50) for recently synthesized ferulic acid derivatives by machine learning with molecular orbital energy as an explanatory variable and IC50 as an objective variable. We compared 96 regression models including xgbLinear and neuralnet included in R/caret package. We were able to construct IC50 prediction models for these new ferulic acids by using xgbLinear, M5, ppr, and neuralnet as regression methods.
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Prediction of log P Parameter Using Molecular Orbital Energies and Machine Learning 招待あり 査読あり
Hiroyuki Teramae
Journal of Computer Chemistry, Japan 22 ( 2 ) 34 - 36 2024年02月
担当区分：筆頭著者, 責任著者 記述言語：日本語 掲載種別：研究論文（学術雑誌）
Octanol/water partition coefficient, log P, is an important parameter in classical QSAR. The new method using machine learning which we propose uses only the molecular orbital energy as an explanatory variable and does not include log P. Therefore, since the log P value can be predicted using the molecular orbital energy, we speculated that log P may not be necessary as a result if sufficient number of molecular orbital energies would be given as parameters.
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Prediction of Entropy by Machine Learning with Molecular Orbital Energies 招待あり 査読あり
Takafumi Yuuki, Wakana Nakahara, Hiroyuki Teramae
Journal of Computer Chemistry, Japan 22 ( 2 ) 31 - 33 2024年02月
担当区分：責任著者 記述言語：日本語 掲載種別：研究論文（学術雑誌）
The values of the entropy of 148 small organic molecules have been estimated by machine learning with only molecular orbital energies as the explanatory variables. Out of 148 molecules, we used 104 molecules for the training set and 44 molecules for the test set. We used 139 regression methods of R/caret packege for machine learning. We evaluated values by RMSE (Root Mean Squared Error) and R² (coefficient of determination). From those evaluation, xgbLinear (eXtreme Gradient Boosting) and RRFglobal (Regularized Random Forest) are considered better than other regression methods. It has been proved that the entropy can be predicted by the molecular orbital energies only.
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Machine Learning Study of Antioxidant Effects with Molecular Orbital Energies as Explanatory Variables 招待あり 査読あり
Hiroyuki Teramae, Meiyan Xuan, Jun Takayama, Mari Okazaki, Takeshi Sakamoto
Journal of Computer Chemistry, Japan 21 ( 4 ) 103 - 105 2023年04月
担当区分：筆頭著者, 責任著者 記述言語：日本語 掲載種別：研究論文（学術雑誌）
The values of the internuclear distances and the dipole moments of 14 small molecules have been estimated by machine learning with only molecular orbital energies as the explanatory variables. We use four regression methods, partial least square (PLS), random forest (RF), Radial Basis Function Kernel Regularized Least Squares (krlsRadial), and Baysian Regularized Neural Networks (BRNN) and we report only BRNN results for the internuclear distances, and PLS results for the dipole moments. The coefficients of determination for the internulear distances and the dipole moments are 0.9318 and 0.7265, respectively. It has been proved that the internuclear distances and the dipole moments can be predicted by the molecular orbital energies only.
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Prediction of molecular properties with machine learning and molecular orbital energies 招待あり 査読あり
Hiroyuki Teramae, Meiyan Xuan, Jun Takayama, Mari Okazaki and Takeshi Sakamoto
AIP Conference Proceedints 2611 02007 2022年11月
担当区分：筆頭著者, 責任著者 記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
The prediction by the machine learning using molecular orbital energies as an explanatory variable is attempted to predict the strength of anxiolytics, anti-anxiety, and muscle relaxant of benzodiazepine anxiolytics. We also attempt to predict half-life of concentration in the body T1/2, and time to reach maximum body concentration Tmax of benzodiazepine anxiolytics with the same procedure. The molecular orbital calculations are performed at 6-31G(d, p) level and random forest is used as regression method. The number of molecular orbitals is varied from 2 to 20 and it is found that 4 or 6 is almost sufficient for the prediction of these 5 objective variables. Finally, the predictions of five properties in the present study are fairly well agreed with the experiments by machine learning employing the molecular orbital energies as the only explanatory variables.
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Possible Prediction of Molecular Properties with Machine Learning and Molecular Orbital Energies 招待あり 査読あり
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年09月
担当区分：筆頭著者 記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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分子軌道エネルギーと機械学習による分子物性の予測 査読あり
寺前 裕之, 松尾 哲秀, 庭月野 一眞, 井上 竜太, 野口 晋治、玄 美燕, 山下 司, 高山 淳, 岡﨑 真理, 坂本 武史
19 ( 2 ) 43 - 45 2020年
担当区分：筆頭著者 記述言語：日本語 掲載種別：研究論文（学術雑誌）
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Ab initio electronic structure calculation of polymononucleotide, a model of B-type DNA 招待あり 査読あり 国際誌
Hiroyuki Teramae, Yuriko Aoki
AIP Conference Proceedings 2040 ( 1 ) 020013 2018年
担当区分：筆頭著者 記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
DOI： 10.1063/1.5079055
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Hamiltonian algorithm and its application to the aromatic oxidative cyclization on N-methoxy-N-prenylbenzamide 査読あり
Hiroyuki Teramae, Kousuke Hayashi, Jun Takayama, Takeshi Sakamoto
AIP Conference Proceedings 1790 020024 2016年
担当区分：筆頭著者, 責任著者 記述言語：英語 掲載種別：研究論文（学術雑誌）
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A Molecular Orbital Study of the Dipole Moment of HF, LiH, and HeH+ 査読あり
Hiroyuki TERAMAE, Yasuko Y. MARUO, Jiro NAKAMURA
Chemistry Letters 41 1642 - 1643 2012年
担当区分：筆頭著者, 責任著者 記述言語：英語 掲載種別：研究論文（学術雑誌）
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Molecular structure optimization and molecular dynamics using hamiltonian algorithm: Structure of benzodiazepine minor tranquilizers-towards non-empirical drug design- 査読あり
Teramae, H., Ohtawara, K., Ishimoto, T., Nagashima, U.
Bulletin of the Chemical Society of Japan 81 1094 - 1102 2008年01月
担当区分：筆頭著者 記述言語：英語 掲載種別：研究論文（学術雑誌）
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Study on optimization of molecular structure using Hamiltonian algorithm 査読あり
Ohtawara, K., Teramae, H.
Chemical Physics Letters 390 84 - 88 2004年01月
担当区分：最終著者, 責任著者 記述言語：英語 掲載種別：研究論文（学術雑誌）
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Parallel Processing on ab initio Crystal Orbital Calculations of One-Dimensional Polymers, Part2 査読あり
H. Teramae
Journal of Chemical Software 6 75 - 84 2000年01月
担当区分：筆頭著者, 責任著者 記述言語：日本語 掲載種別：研究論文（学術雑誌）
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Geometry of the localized σ-σ* excited state of n-tetrasilane 査読あり
Teramae, H., Michl, J.
Chemical Physics Letters 276 127 - 132 1997年01月
担当区分：筆頭著者 記述言語：英語 掲載種別：研究論文（学術雑誌）
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Ab initio study on electronic structures of σ-conjugated silicon polymers. 査読あり
H. Teramae
Proceeding of the 5th Asia Pacific Physics Conference 2 693 - 703 1994年01月
担当区分：筆頭著者, 責任著者 記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Electronic states of linear tetrasilane and polysilanes 査読あり
H. Teramae, J. Michl
Mol.Cryst.Liq.Cryst. 256 149 - 159 1994年01月
担当区分：筆頭著者 記述言語：英語 掲載種別：研究論文（学術雑誌）
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Comment on ''Electronic properties of polymeric silicon hydrides'' 査読あり
Teramae, H.
Physical Review B 46 12788 - 12789 1992年01月
記述言語：英語 掲載種別：研究論文（学術雑誌）
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Ab initio studies on silicon compounds. 2. On the gauche structure of the parent polysilane 査読あり
Teramae, H., Takeda, K.
Journal of the American Chemical Society 111 1281 - 1285 1989年01月
記述言語：英語 掲載種別：研究論文（学術雑誌）
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AB initio study on the CIS-TRANS energetics of polyacetylene 査読あり
Teramae, H.
Synthetic Metals 19 1004 1987年01月
記述言語：英語 掲載種別：研究論文（学術雑誌）
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Ab initio studies on the silicon compound: On the electronic structure of disilene reconsidered 査読あり
Teramae, H.
Journal of the American Chemical Society 109 4140 - 4142 1987年01月
記述言語：英語 掲載種別：研究論文（学術雑誌）