QSAR Model Study of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole of Cystic-
brosis-transmembrane Conductance-regulator Gene Potentiators

Yaru, Si; Ma, Kang; Hu, Yingfeng; Si, Hongzong; Zhai, Hongbin

doi:10.2174/1570180818666211022142920

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Based on the experiments of Si Y et al (Si et al, 2022) and Chen C et al (Chen and Si, 2021) the fitting ability of the non-linear model constructed by GEP is acceptable.…”

Section: Gepmentioning

confidence: 93%

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma

Rui

Gao

et al. 2023

Front. Pharmacol.

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Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children’s eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents.Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models.Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed.Result: The best linear model created using HM had an R2, S2, and R2cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q2 (0.503), R2 (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model’s reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability.Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.

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“…Based on the experiments of Si Y et al (Si et al, 2022) and Chen C et al (Chen and Si, 2021) the fitting ability of the non-linear model constructed by GEP is acceptable.…”

Section: Gepmentioning

confidence: 93%

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma

Rui

Gao

et al. 2023

Front. Pharmacol.

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“…And the biological and chemical properties of compounds are portrayed by molecular descriptors in QSAR model (Jin et al, 2022;LI et al, 2023). Therefore, QSAR can be used to predict the biological activity of new compounds by mathematical models based on precisely selected molecular descriptors (Vilar et al, 2008;Si et al, 2022). QSAR shows a strong ability in new drug research, which optimizes pharmacodynamic characteristics and reduces expensive experiments in the meanwhile.…”

Section: Introductionmentioning

confidence: 99%

Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO

Xue,

Zhang,

Yan

et al. 2024

Front. Pharmacol.

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PARP1 is one of six enzymes required for the highly error-prone DNA repair pathway microhomology-mediated end joining (MMEJ) and needs to be inhibited when over-expressed. In order to study the PARP1 inhibitory effect of fused tetracyclic or pentacyclic dihydrodiazepinoindolone derivatives (FTPDDs) by quantitative structure-activity relationship technique, six models were established by four kinds of methods, heuristic method, gene expression programming, random forester, and support vector regression with single, double, and triple kernel function respectively. The single, double, and triple kernel functions were RBF kernel function, the integration of RBF and polynomial kernel functions, and the integration of RBF, polynomial, and linear kernel functions respectively. The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. Among the models, the model established by support vector regression with triple kernel function, in which the optimal R2 and RMSE of training set and test set were 0.9353, 0.9348 and 0.0157, 0.0288, and R2cv of training set and test set were 0.9090 and 0.8971, shows the strongest prediction ability and robustness. The method of support vector regression with triple kernel function is a great promotion in the field of quantitative structure-activity relationship, which will contribute a lot to designing and screening new drug molecules. The information contained in the model can provide important factors that guide drug design. Based on these factors, six new FTPDDs have been designed. Using molecular docking experiments to determine the properties of new derivatives, the new drug was ultimately successfully designed.

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“…Amide derivatives are a newly discovered type of XO inhibitors that have significant research value. Although assessing the inhibitory effect of XO (

) is a time-consuming and labor-intensive process, models based on the quantitative structure–activity relationship (QSAR) theory can predict the biological activity of new compounds precisely and quickly by constructing the quantitative relationship between chemical structure and biological activity ( Si et al, 2021a ; Chen et al, 2021 ). By constructing quantitative relationships and machine learning techniques, researchers can explore large datasets and accurately and quickly predict the biological activity of new compounds, which is of great significance for developing new drugs and saving human and material resources ( Chen et al, 2022 ).…”

Section: Introductionmentioning

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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QSAR Model Study of 2,3,4,5-tetrahydro-1H-pyrido[4,3-b]indole of Cystic- brosis-transmembrane Conductance-regulator Gene Potentiators

Cited by 4 publications

References 18 publications

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma

Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO

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

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