Novel quantitative structure–activity relationship model to predict activities of natural products against COVID‐19

Yaru, Si; Xu, Xinyue; Hu, Yingfeng; Si, Hongzong; Zhai, Hongbin

doi:10.1111/cbdd.13822

Cited by 8 publications

(4 citation statements)

References 17 publications

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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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“…Linear Model by HM 21,22) HM is a common and effective method for selecting descriptors, which has been proved in some previous literatures. [23][24][25][26][27] Therefore, HM was adopted to determine the concrete descriptors calculated from CODESSA in previous step. This method is not only effective but also has no limitation on the size of the dataset.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Anti-proliferation Effect of [1,2,3]Triazolo[4,5-<i>d</i>]pyrimidine Derivatives by Random Forest and Mix-Kernel Function SVM with PSO

Gao

Xia

Zhang

2022

CHEMICAL & PHARMACEUTICAL BULLETIN

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In order to predict the anti-gastric cancer effect of [1,2,3]triazolo[4,5-d]pyrimidine derivatives (1,2,3-TPD), quantitative structure-activity relationship (QSAR) studies were performed. Based on five descriptors selected from descriptors pool, four QSAR models were established by heuristic method (HM), random forest (RF), support vector machine with radial basis kernel function (RBF-SVM), and mix-kernel function support vector machine (MIX-SVM) including radial basis kernel and polynomial kernel function. Furthermore, the model built by RF explained the importance of the descriptors selected by HM. Compared with RBF-SVM, the MIX-SVM enhanced the generalization and learning ability of the constructed model simultaneously and the multi parameters optimization problem in this method was also solved by particle swarm optimization (PSO) algorithm with very low complexity and fast convergence. Besides, leave-one-out cross validation (LOO-CV) was adopted to test the robustness of the models and Q 2 was used to describe the results. And the MIX-SVM model showed the best prediction ability and strongest model robustness: R 2 0.927, Q 2 0.916, mean square error (MSE) 0.027 for the training set and R 2 0.946, Q 2 0.913, MSE 0.023 for the test set. This study reveals five key descriptors of 1,2,3-TPD and will provide help to screen out efficient and novel drugs in the future.

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“…After generating molecular descriptors, HM in CODESSA software was used to accomplish the pre-selection of the descriptors and build the linear model (Si et al, 2021).…”

Section: Linear Model By Hmmentioning

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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Novel quantitative structure–activity relationship model to predict activities of natural products against COVID‐19

Cited by 8 publications

References 17 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

Prediction of Anti-proliferation Effect of [1,2,3]Triazolo[4,5-<i>d</i>]pyrimidine Derivatives by Random Forest and Mix-Kernel Function SVM with PSO

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

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