Docking of Potent Anticancer Agents; 4-(Pyrazol-4yl)-Pyrimidine Derivatives as Selective Cyclin-Dependent Kinase 4/6 Inhibitors

Akrimah,; Tjahyono, Daryono Hadi; Musadad, Amir

doi:10.7763/ijcea.2013.v4.338

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…RMSD, root mean square deviation, is considered as a necessary condition to monitor how much the structure has deviated from its initial geometry [ 25 ]. If the RMSD between its docking pose and crystallographic orientation is less than 2.0 Å, the docking protocol is correct and can be used to dock other ligands to the receptor protein [ 26 ].…”

Section: Resultsmentioning

confidence: 99%

In Silico Discovery of Potential VEGFR-2 Inhibitors from Natural Derivatives for Anti-Angiogenesis Therapy

Zhou

Luo

et al. 2014

IJMS

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Angiogenesis is the growth of new capillaries from existing blood vessels that supply oxygen and nutrients and provide gateways for immune surveillance. Abnormal vessel growth in term of excessive angiogenesis is a hallmark of cancer, inflammatory and eye diseases. VEGFR-2 (vascular endothelial growth factor receptor 2) dominating the process of angiogenesis has led to approval of therapeutic inhibitors and is becoming a promising target for anti-angiogenic drugs. Notwithstanding these successes, the clinical use of current VEGFR-2 blockers is more challenging than anticipated. Taking axitinib as a reference drug, in our study we found three potent VEGFR-2 inhibitors (ZINC08254217, ZINC08254138, and ZINC03838680) from natural derivatives. Each of the three inhibitors acquired a better grid score than axitinib (−62.11) when docked to VEGFR-2. Molecular dynamics simulations demonstrated that ZINC08254217– and ZINC08254138–VEGFR-2 complexes were more stable than axitinib. Similar to bind free energy for axitinib (−54.68 kcal/mol), such for ZINC03838680, ZINC08254217, and ZINC08254138 was −49.37, −43.32, and −32.73 kcal/mol respectively. These results suggested these three compounds could be candidate drugs against angiogenesis, with comparable VEGFR-2 binding affinity of axitinib. Hence findings in our study are able to provide valuable information on discovery of effective anti-angiogenesis therapy.

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Section: Resultsmentioning

confidence: 99%

In Silico Discovery of Potential VEGFR-2 Inhibitors from Natural Derivatives for Anti-Angiogenesis Therapy

Zhou

Luo

et al. 2014

IJMS

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“…48 On the other hand, Akrimah et al merged MD with QSAR in order to discover in silico three new 4-(pyrazol-4-yl)-pyrimidine derivatives. 49 Despite the diversity of available various methods, there is no work fulfilling all of the requirements for predicting the binding activity between CDKs and ligands. This fact motivated us to develop a new prediction method with reliable improved classification technique.…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, Kurapati et al tried to use cross-docking to select CDK9 protein target using virtual screening . On the other hand, Akrimah et al merged MD with QSAR in order to discover in silico three new 4-(pyrazol-4-yl)-pyrimidine derivatives . Despite the diversity of available various methods, there is no work fulfilling all of the requirements for predicting the binding activity between CDKs and ligands.…”

Section: Introductionmentioning

confidence: 99%

Binding Activity Prediction of Cyclin-Dependent Inhibitors

Saha

Rak

Bhowmick

et al. 2015

J. Chem. Inf. Model.

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The Cyclin-Dependent Kinases (CDKs) are the core components coordinating eukaryotic cell division cycle. Generally the crystal structure of CDKs provides information on possible molecular mechanisms of ligand binding. However, reliable and robust estimation of ligand binding activity has been a challenging task in drug design. In this regard, various machine learning techniques, such as Support Vector Machine, Naive Bayesian classifier, Decision Tree, and K-Nearest Neighbor classifier, have been used. The performance of these heterogeneous classification techniques depends on proper selection of features from the data set. This fact motivated us to propose an integrated classification technique using Genetic Algorithm (GA), Rotational Feature Selection (RFS) scheme, and Ensemble of Machine Learning methods, named as the Genetic Algorithm integrated Rotational Ensemble based classification technique, for the prediction of ligand binding activity of CDKs. This technique can automatically find the important features and the ensemble size. For this purpose, GA encodes the features and ensemble size in a chromosome as a binary string. Such encoded features are then used to create diverse sets of training points using RFS in order to train the machine learning method multiple times. The RFS scheme works on Principal Component Analysis (PCA) to preserve the variability information of the rotational nonoverlapping subsets of original data. Thereafter, the testing points are fed to the different instances of trained machine learning method in order to produce the ensemble result. Here accuracy is computed as a final result after 10-fold cross validation, which also used as an objective function for GA to maximize. The effectiveness of the proposed classification technique has been demonstrated quantitatively and visually in comparison with different machine learning methods for 16 ligand binding CDK docking and rescoring data sets. In addition, the best possible features have been reported for CDK docking and rescoring data sets separately. Finally, the Friedman test has been conducted to judge the statistical significance of the results produced by the proposed technique. The results indicate that the integrated classification technique has high relevance in predicting of protein-ligand binding activity.

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In-silico interaction of antimalarial activity of secondary metabolic compounds Ginkgo biloba L. against wild type protein Plasmodium falciparum dihydrofolate reductase-thymidylate synthase (PfDHFR-TS)

Sandy¹,

Sasto²

2022

10th International Conference on Applied Science and Technology

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Docking of Potent Anticancer Agents; 4-(Pyrazol-4yl)-Pyrimidine Derivatives as Selective Cyclin-Dependent Kinase 4/6 Inhibitors

Cited by 4 publications

References 14 publications

In Silico Discovery of Potential VEGFR-2 Inhibitors from Natural Derivatives for Anti-Angiogenesis Therapy

In Silico Discovery of Potential VEGFR-2 Inhibitors from Natural Derivatives for Anti-Angiogenesis Therapy

Binding Activity Prediction of Cyclin-Dependent Inhibitors

In-silico interaction of antimalarial activity of secondary metabolic compounds Ginkgo biloba L. against wild type protein Plasmodium falciparum dihydrofolate reductase-thymidylate synthase (PfDHFR-TS)

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