A Genetic Algorithm- Back Propagation Artificial Neural Network Model to Quantify the Affinity of Flavonoids Toward P-Glycoprotein

Shen, Jibin; Cui, Ying; Gu, Jun; Li, Yaxiao; Li, Lingzhi

doi:10.2174/1386207311301010002

Cited by 15 publications

(8 citation statements)

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“…Hence, classification of Pgp-interacting compounds is challenging (Wang et al, 2005[ 49 ]) and is a growing research area. Recently, many computational approaches such as quantitative structure activity relationship (Ghandadi et al, 2014[ 13 ]; Palestro et al, 2014[ 30 ]; Shen et al, 2014[ 38 ]), classification models (Adenot and Lahana, 2004[ 2 ]; Chen et al, 2011[ 8 ]; Klepsch et al, 2014[ 17 ]; Levatić et al, 2013[ 23 ]; Li et al, 2014[ 24 ]; Penzotti et al, 2002[ 31 ]; Prachayasittikul et al, 2015[ 34 ]; Wang et al, 2011[ 51 ]), molecular docking (Ghandadi et al, 2014[ 13 ]; Palestro et al, 2014[ 30 ]; Zeino et al, 2014[ 53 ]), and substructure analysis (Prachayasittikul et al, 2016[ 33 ]; Wang et al, 2011[ 51 ]; Klepsch et al, 2014[ 17 ]) have been successfully employed to provide deeper understanding about this promiscuous protein.…”

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confidence: 99%

P-glycoprotein transporter in drug development

Prachayasittikul

2016

EXCLI Journal; 15:Doc113; ISSN 1611-2156

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confidence: 99%

P-glycoprotein transporter in drug development

Prachayasittikul

2016

EXCLI Journal; 15:Doc113; ISSN 1611-2156

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“…It has been used in engineering and business applications, and is recently also adopted in biomedical research. [18][19][20] Before BPANN analysis, we also applied GA, a stochastic method applying Darwin's evolution hypothesis and used as the variable selection methods optimizing by the crossover and mutation operation. 18,21,22) Using these statistical methods, we finally succeeded in constructing a good model (Q=0.822) according to the classification attained by PCA.…”

Section: Discussionmentioning

confidence: 99%

Quality Characterization of Japanese Medicinal Paeoniae Radix by Metallomic Analysis

Shimada-Takaura

Nakamura

Kawase

et al. 2018

CHEMICAL & PHARMACEUTICAL BULLETIN

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Paeoniae Radix is one of the crude drugs frequently used in traditional Japanese medicine (Kampo medicine). It takes abundant labor and time to cultivate Paeonia lactiflora for medicinal use; high production cost is one of the main reasons why the domestic production of Paeoniae Radix is decreasing in Japan. To promote the production of Paeoniae Radix, we focused on Paeonia cultivars that produce commercially valuable flowers and investigated their possibility for medicinal use. We prepared 28 batches of peony roots derived from P. lactiflora, which were cultivated in Japan; 4 batches were crude drug samples, and 24 batches were cultivar roots. The elements contained in these samples were measured using inductively coupled plasma (ICP)-MS. The obtained data were then analyzed by principal component analysis (PCA) and back propagation artificial neural network (BPANN) analysis. No significant differences were found between the profiles of elements contained in crude drugs and cultivar roots. However, PCA results indicated a high similarity of the multielement fingerprints of crude drugs. Using the PCA results, we also assessed visible cluster trends and found that 5 batches of cultivars also showed fingerprints related to those of crude drugs. We certified this classification by BPANN. From the perspective of metallomics, our findings suggest that these 5 batches of Paeonia cultivars could be alternatives to crude drugs.

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“…CV values as indicator or even as the ultimate proof of the high predictive power of a QSAR/QSPR model [29]. In recent years, some authors demonstrated that a high value of Q 2 LOO appears to be necessary but not a sufficient condition for the model to have a high predictive power [21,30,31].…”

Section: Many Authors Consider High Qmentioning

confidence: 99%

QSPR Models to Predict Thermodynamic Properties of Cycloalkanes Using Molecular Descriptors and GA-MLR Method

Joudaki

Shafiei

2020

CAD

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Aims and Objectives: QSPR models establish relationships between different types of structural information to their observed properties. In the present study the relationship between the molecular descriptors and quantum properties of cycloalkanes is represented. Materials and Methods: Genetic Algorithm (GA) and Multiple Linear Regressions (MLR) were successfully developed to predict quantum properties of cycloalkanes. A large number of molecular descriptors were calculated with Dragon software and a subset of calculated descriptors was selected with a genetic algorithm as a feature selection technique. The quantum properties consist of the heat capacity (Cv)/ Jmol-1K-1 entropy(S)/ Jmol-1K-1 and thermal energy(Eth)/ kJmol-1 were obtained from quantum-chemistry technique at the Hartree-Fock (HF) level using the ab initio 6-31G* basis sets. Results: The Genetic Algorithm (GA) method was used to select important molecular descriptors and then they were used as inputs for SPSS software package. The predictive powers of the MLR models were discussed using Leave-One-Out (LOO) cross-validation, leave-group (5-fold)-out (LGO) and external prediction series. The statistical parameters of the training and test sets for GA–MLR models were calculated. Conclusion: The resulting quantitative GA-MLR models of Cv, S, and Eth were obtained:[r2=0.950, Q2=0.989, r2 ext=0.969, MAE(overall,5-flod)=0.6825 Jmol-1K-1], [r2=0.980, Q2=0.947, r2 ext=0.943, MAE(overall,5-flod)=0.5891Jmol-1K-1], and [r2=0.980, Q2=0.809, r2 ext=0.985, MAE(overall,5-flod)=2.0284 kJmol-1]. The results showed that the predictive ability of the models was satisfactory, and the constitutional, topological indices and ring descriptor could be used to predict the mentioned properties of 103 cycloalkanes.

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A Genetic Algorithm- Back Propagation Artificial Neural Network Model to Quantify the Affinity of Flavonoids Toward P-Glycoprotein

Cited by 15 publications

References 0 publications

P-glycoprotein transporter in drug development

P-glycoprotein transporter in drug development

Quality Characterization of Japanese Medicinal Paeoniae Radix by Metallomic Analysis

QSPR Models to Predict Thermodynamic Properties of Cycloalkanes Using Molecular Descriptors and GA-MLR Method

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