Design, Synthesis, and Potent Antiepileptic Activity with Latent Nerve Rehabilitation of Novel γ-Aminobutyric Acid Derivatives

He, Deyan; Ma, Jing; Shi, Xiuxiao; Zhao, Chunyan; Hou, Meng; Guo, Qingxin; Ma, Shangxian; Li, Xiaojun; Zhao, Ping; Liu, Wenhu; Yang, Zhilong; Mou, Jianping; Song, Peng; Zhang, Yang; Li, Jing

doi:10.1248/cpb.c14-00329

Cited by 7 publications

(2 citation statements)

References 53 publications

(65 reference statements)

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“…The heuristic method, an efficient approach for descriptor selection and linear model construction, has no limitation to the size of the dataset ( He et al, 2014 ; Si et al, 2021b ). Before executing the model establishment algorithm, descriptors should be pre-selected under the guidance of the following principles: non-generic descriptors and descriptors with a constant value should be removed, and descriptors with a large correlation coefficient (greater than 0.8) should also be discarded ( Si et al, 2021b ).…”

Section: Methodsmentioning

confidence: 99%

Quantitative structure–activity relationship study of amide derivatives as xanthine oxidase inhibitors using machine learning

et al. 2023

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The target of the study is to predict the inhibitory effect of amide derivatives on xanthine oxidase (XO) by building several models, which are based on the theory of the quantitative structure–activity relationship (QSAR). The heuristic method (HM) was used to linearly select descriptors and build a linear model. XGBoost was used to non-linearly select descriptors, and radial basis kernel function support vector regression (RBF SVR), polynomial kernel function SVR (poly SVR), linear kernel function SVR (linear SVR), mix-kernel function SVR (MIX SVR), and random forest (RF) were adopted to establish non-linear models, in which the MIX-SVR method gives the best result. The kernel function of MIX SVR has strong abilities of learning and generalization of established models simultaneously, which is because it is a combination of the linear kernel function, the radial basis kernel function, and the polynomial kernel function. In order to test the robustness of the models, leave-one-out cross validation (LOOCV) was adopted. In a training set, R2 = 0.97 and RMSE = 0.01; in a test set, R2 = 0.95, RMSE = 0.01, and Rcv2 = 0.96. This result is in line with the experimental expectations, which indicate that the MIX-SVR modeling approach has good applications in the study of amide derivatives.

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Section: Methodsmentioning

confidence: 99%

Quantitative structure–activity relationship study of amide derivatives as xanthine oxidase inhibitors using machine learning

et al. 2023

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“…近年来发现阿司匹林具有改善精神疾病的作用, 如长期服用低剂量阿司匹林可能保护神经 [79] , 有预防阿尔兹海默病及改善抑郁的可能性 [80] . He 等 [81] 此后, 多种金属配合物, 如 Pt、Pd、Au 与 Ru 等相继被设计为抗癌药物并应用于临床 [86] . 而金属化合物与阿司匹林的结合, 也成为了开发新型抗肿瘤药物的途径.…”

Section: 类似地在水杨醛和阿司匹林的基础上合成了四种unclassified

Advances in the Study of Structural Modification of Aspirin and Their Biological Activities

Zhang¹,

Gao²,

Zhang³

et al. 2020

Chin. J. Org. Chem.

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Aspirin (ASP), the first synthetic drug, is widely used as a non steroidal anti-inflammatory drug. It displays a variety of biological activities, such as anti-thrombosis, anti-inflammatory, anti-tumor, etc. A lot of works about the synthesis and related activity evaluation of its derivatives were reported. There are four kinds of derivatization methods: skeleton derivatization, prodrug derivatization, twin derivatization and metal coordination derivatization. According to the different modification sites, skeleton derivatization could be further divided into C(1)-COOH site modification, C(1)-COOH site and C(2)-OAc site simultaneous modification, C(2)-OAc site modification and benzene ring modification. NO-ASP is the main method to prepare antithrombotic derivatives, and metal coordination modification is the main synthesis scheme of anticancer derivatives. The structure modification and bioactivity research of aspirin in recent twenty years and the synthetic routes of 353 aspirin derivatives and the pharmacological activities of some derivatives are described, which provides a reference for the further development of aspirin derivatives.

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Advances and Perspectives of Gamma-Aminobutyric Acid as a Bioactive Compound in Food

Jain

Ghodke

2020

Advanced Structured Materials

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Design, Synthesis, and Potent Antiepileptic Activity with Latent Nerve Rehabilitation of Novel γ-Aminobutyric Acid Derivatives

Cited by 7 publications

References 53 publications

Quantitative structure–activity relationship study of amide derivatives as xanthine oxidase inhibitors using machine learning

Quantitative structure–activity relationship study of amide derivatives as xanthine oxidase inhibitors using machine learning

Advances in the Study of Structural Modification of Aspirin and Their Biological Activities

Advances and Perspectives of Gamma-Aminobutyric Acid as a Bioactive Compound in Food

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