This experiment has been carried out to observe the potential thrombolytic activity of naturally occuring phytochemicals in Ginger (Zingiber officinale) and to analyze their drug likeness property and ADME/T profile. ...This experiment has been carried out to observe the potential thrombolytic activity of naturally occuring phytochemicals in Ginger (Zingiber officinale) and to analyze their drug likeness property and ADME/T profile. Thrombolytic activity of Ginger has already been confirmed in laboratory experiment and this study focuses on the molecular interactions among four phytocompounds (Isovanillin, Gingerol, Beta-sitosterol and 2,6-Dimethyl-2-octene-1,8-diol) found in Ginger and Tissue Plasminogen Activator (tPA). Present experiment is largely based on computer-aided drug design protocol where the strength of interaction is described as binding energy function. Isovanillin exhibited better docking score, and so this compound might have greater thrombolytic activity than others. Moreover, Isovanillin also suggested sound drug likeness property and ADME/T profile which predicts its safeness for consumption in human body. But Beta-sitosterol violated Lipinski’s rule of five and 2, 6-Dimethyl-2-octene-1,8-diol showed the lowest affinity of binding with tPA. However, further in vivo or in vitro study may be required to confirm the thrombolytic activity of Isovanillin.展开更多
Cancer comprises a group of diseases which are involved in the aberrant growth of the cells causing disruption of normal body function. Due to the lack of proper sophisticated treatments this nasty disease leads to th...Cancer comprises a group of diseases which are involved in the aberrant growth of the cells causing disruption of normal body function. Due to the lack of proper sophisticated treatments this nasty disease leads to the death of most of the patients affected with it. Moreover, treatments like chemotherapy involve other post-treatment complications which make them unfavorable for extended use. Medicinal plants possess many phytochemicals of great therapeutic value and many of them are effective in killing cancer cells. These compounds working by variety of mechanisms and in most of the cases exhibit their anticancer potentiality by inhibiting many proteins involved in cell growth and division. Molecular docking is a computational approach which facilitates the finding of the best molecule from a group which may bind with the highest affinity with the intended target by providing a virtual biological system. This process works on the basis of specific algorithm and involves scoring function to rank the molecules that fit with the target. This study has been designed to investigate the potentiality of four phytochemicals from Clitoria ternatea—Kaempferol, Myricetin, P-Hydroxycinnamic acid and Quercetin as inhibitors of two cell cycle checkpoint proteins—Cyclin Dependent Kinase-2 (CDK-2) and Cyclin Dependent Kinase-6 (CDK-6) in Cyclin/CDK pathway. Quercetin and Myricetin docked with higher affinity with CDK-2 and CDK-6 respectively. Drug likeness property analysis and ADME/T test impose computational approach to investigate physicochemical and pharmacological properties of candidate drug molecules. P-Hydroxycinnamic acid performed well in both drug likeness property analysis and ADME/T than Quercetin and Myricetin. So, P-Hydroxycinnamic acid is the best finding of this experiment.展开更多
Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage...Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.展开更多
文摘This experiment has been carried out to observe the potential thrombolytic activity of naturally occuring phytochemicals in Ginger (Zingiber officinale) and to analyze their drug likeness property and ADME/T profile. Thrombolytic activity of Ginger has already been confirmed in laboratory experiment and this study focuses on the molecular interactions among four phytocompounds (Isovanillin, Gingerol, Beta-sitosterol and 2,6-Dimethyl-2-octene-1,8-diol) found in Ginger and Tissue Plasminogen Activator (tPA). Present experiment is largely based on computer-aided drug design protocol where the strength of interaction is described as binding energy function. Isovanillin exhibited better docking score, and so this compound might have greater thrombolytic activity than others. Moreover, Isovanillin also suggested sound drug likeness property and ADME/T profile which predicts its safeness for consumption in human body. But Beta-sitosterol violated Lipinski’s rule of five and 2, 6-Dimethyl-2-octene-1,8-diol showed the lowest affinity of binding with tPA. However, further in vivo or in vitro study may be required to confirm the thrombolytic activity of Isovanillin.
文摘Cancer comprises a group of diseases which are involved in the aberrant growth of the cells causing disruption of normal body function. Due to the lack of proper sophisticated treatments this nasty disease leads to the death of most of the patients affected with it. Moreover, treatments like chemotherapy involve other post-treatment complications which make them unfavorable for extended use. Medicinal plants possess many phytochemicals of great therapeutic value and many of them are effective in killing cancer cells. These compounds working by variety of mechanisms and in most of the cases exhibit their anticancer potentiality by inhibiting many proteins involved in cell growth and division. Molecular docking is a computational approach which facilitates the finding of the best molecule from a group which may bind with the highest affinity with the intended target by providing a virtual biological system. This process works on the basis of specific algorithm and involves scoring function to rank the molecules that fit with the target. This study has been designed to investigate the potentiality of four phytochemicals from Clitoria ternatea—Kaempferol, Myricetin, P-Hydroxycinnamic acid and Quercetin as inhibitors of two cell cycle checkpoint proteins—Cyclin Dependent Kinase-2 (CDK-2) and Cyclin Dependent Kinase-6 (CDK-6) in Cyclin/CDK pathway. Quercetin and Myricetin docked with higher affinity with CDK-2 and CDK-6 respectively. Drug likeness property analysis and ADME/T test impose computational approach to investigate physicochemical and pharmacological properties of candidate drug molecules. P-Hydroxycinnamic acid performed well in both drug likeness property analysis and ADME/T than Quercetin and Myricetin. So, P-Hydroxycinnamic acid is the best finding of this experiment.
基金supported by the National Natural Science Foundation of China (21210003 and 81230076 to H.J., 81773634 to M.Z. and 81430084 to K.C.)the “Personalized Medicines-Molecular Signature-based Drug Discovery and Development”, Strategic Priority Research Program of the Chinese Academy of Sciences (XDA12050201 to M.Z.)+1 种基金National Key Research & Development Plan (2016YFC1201003 to M.Z.)the National Basic Research Program (2015CB910304 to X.L.)
文摘Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials.Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.
