月別: 2021年6月

[Published online Journal of Computer Chemistry, Japan Vol.20, 1-9, by J-STAGE]
<Title:> 化合物のAmes予測におけるGraph Convolutional Networkの特徴評価
<Author(s):> 半田　千彰, 小沢　知永, 福澤　薫, 米持　悦生
<Corresponding author E-Mill:> chiaki_handa(at)pharm.kissei.co.jp
<Abstract:> 医薬品候補物質の潜在的な発がん性早期警戒システムであるAmes試験のin silico予測は，創薬研究において重要な予測項目の一つである．in silico予測の一手法である機械学習による予測では，Applicability Domain (AD)という機械学習モデルが本来の性能を発揮できるデータ領域を定義する研究がある．創薬研究においては，学習データと構造類似性が低い医薬品候補化合物の予測を行う場合があり，そのような化合物はAD領域外になる可能性が高く予測精度が低下する傾向がある．本研究では，Ames試験の機械予測モデルを作成し，テストデータとしてAD領域内/外となる確率が高い化合物群をそれぞれ用意して，複数の機械学習手法の予測性能を評価した．人工知能技術の発展により，創薬分野でも注目を集めているGraph Convolutional Network (GCN)と既存の機械学習手法の予測性能を比較した結果，AD領域外となる可能性が高い化合物群の予測性能において，GCNは既存手法より優れていた．
<Keywords:> Keywords Graph Convolutional Network, Machine learning, Ames test, Applicability Domain, Structural similarity
<URL:> https://www.jstage.jst.go.jp/article/jccj/20/1/20_2020-0015/_article/-char/ja/

[Published online Journal of Computer Chemistry, Japan -International Edition Vol.7, -, by J-STAGE]
<Title:> Differences between Gaussian and GAMESS Basis Sets (II) ―6-31G and 6-31G*―
<Author(s):> Munetaka TAKEUCHI, Masafumi YOSHIDA, Umpei NAGASHIMA
<Corresponding author E-Mill:> myoshida(at)tcu.ac.jp
<Abstract:> Gaussian and GAMESS, which are calculation codes for the ab initio molecular orbital method, can be used by simply specifying a basis set name such as 6-31G. However, if an individual basis set with a common name does not have the same parameter set, the calculations with the two codes will each produce a different result. Previously, we used Gaussian and GAMESS for STO-3G calculations of hydrides containing third-period elements and compared the results [J. Comput. Chem. Jpn., 18, 194 (2019)]. In this study, we used 6-31G and 6-31G* for 36 molecules containing a first- to fourth-period element (H, Be, N, Ne, Na-Kr) and compared the results calculated using the two codes. For molecules containing a first- to third-period element (H, Be, N, Ne, Na-Ar) except Si, the optimized structure and total energy obtained with Gaussian and GAMESS were almost the same, whereas the two codes gave different results for K, Ca, and Ga-Kr because the basis parameters used in the two codes are different. On the other hand, the results for the Sc-Zn were in agreement. When the results calculated using Gaussian and GAMESS codes are compared or combined, it is necessary to severe check whether or not the input data produces a sufficiently accurate calculation result.
<Keywords:> Keyword Basis set, Gaussian, GAMESS, 6-31G, 6-31G*, Total energy
<URL:> https://www.jstage.jst.go.jp/article/jccjie/7/0/7_2020-0010/_html

[Published online Journal of Computer Chemistry, Japan -International Edition Vol.7, -, by J-STAGE]
<Title:> Density Functional Study of σ Bond Cleavage in P P Multiple Bond of Phosphinophosphinidene
<Author(s):> Toshiaki MATSUBARA, Keisuke SHIRASAKA
<Corresponding author E-Mill:> matsubara(at)kanagawa-u.ac.jp
<Abstract:> Recently, the synthesis of phosphinophosphinidene, which is a phosphorus analog of carbene, has been reported. Subsequent experimental reports have shown that phosphinophosphinidene acts as an electron acceptor. Because the terminal phosphorus atom inherently acts as an electron donor, chemical reactions may lead to the σ bond cleavage at the phosphorus atom through charge-transfer interaction. In this study, we explore the possibility of the σ bond cleavage in H H, C H, O H, N H, and B H bonds by means of the density functional method using the model molecules, H₂, CH₄, H₂O, NH₃ and BH₃. For H₂ and CH₄, the H H and the C H bonds were found to be broken at the single site of the terminal phosphorus atom by the charge-transfer interactions. The potential energy barrier of about 22 24 kcal/mol is similar to that for carbene, suggesting the possibility of σ bond cleavage in phosphinophosphinidene. In contrast, for H₂O and NH₃, the O H and N H bonds are broken at the two sites of both phosphorus atoms by the abstraction of hydrogen as a proton. In the case of BH₃, cleavage of the B H bond occurs easily at both the single and dual sites of the phosphorus atoms.
<Keywords:> Density functional method, Phosphinophosphinidene, σ Bond cleavage, Reaction mechanism
<URL:> https://www.jstage.jst.go.jp/article/jccjie/7/0/7_2020-0003/_html

[Published online Journal of Computer Chemistry, Japan -International Edition Vol.7, -, by J-STAGE]
<Title:> Materials Informatics Approach to Predictive Models for Elastic Modulus of Polypropylene Composites Reinforced by Fillers and Additives
<Author(s):> Yuko IKEDA, Michihiro OKUYAMA, Yukihito NAKAZAWA, Tomohiro OSHIYAMA, Kimito FUNATSU
<Corresponding author E-Mill:> tomohiro.oshiyama(at)konicaminolta.com
<Abstract:> Advanced processes are useful when developing polymer composites because there are an enormous number of possible combinations of fillers and additives to realize polymers with desired properties. Materials informatics is a data-driven approach to find novel materials or a suitable combination of materials from material data sheets. Here, we used materials informatics to construct a predictive model for the elastic modulus of polypropylene composites. To apply materials informatics to existing experimental data, we described explanatory variables by a combination of 0 and 1 representing polypropylene, or by the content ratio of filler and additive, without using materials property data. We constructed a predictive model for the elastic modulus of polypropylene composites using a partial least square regression model with dummy variables. To validate the predictive model, comparisons were made between measured and predicted elastic moduli for eight new polypropylene composites. The residual was less than 300 MPa for the range 1,000 3,000 MPa. We improved the accuracy of the prediction for composites with high filler content ratio by applying a nonlinear support vector regression model. The predictive model is therefore useful for identifying suitable combinations of polypropylene, filler and additive to achieve a desired elastic modulus.
<Keywords:> Materials informatics, Elastic modulus, Polypropylene composite, PLS, SVR, Dummy variables
<URL:> https://www.jstage.jst.go.jp/article/jccjie/7/0/7_2020-0007/_html