[Published online Journal of Computer Chemistry, Japan Vol.22, 34-36, by J-STAGE]
<Title:> 分子軌道エネルギーを用いた機械学習によるオクタノール/水分配係数log Pの予測
<Author(s):> 寺前 裕之
<Corresponding author E-Mill:> teramae(at)gmail.com
<Abstract:> 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.
<Keywords:> octanol/water partition coefficient, equilibrium geometries, eigenvalues of molecular orbital, machine learning, molecular orbital energies
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0022/_article/-char/ja/
<Title:> 分子軌道エネルギーを用いた機械学習によるオクタノール/水分配係数log Pの予測
<Author(s):> 寺前 裕之
<Corresponding author E-Mill:> teramae(at)gmail.com
<Abstract:> 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.
<Keywords:> octanol/water partition coefficient, equilibrium geometries, eigenvalues of molecular orbital, machine learning, molecular orbital energies
<URL:> https://www.jstage.jst.go.jp/article/jccj/22/2/22_2023-0022/_article/-char/ja/
