Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore （LBP） and structure-based pharmacophore （SBP）. LBP can be utilized to identify active compounds usual with low...Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore （LBP） and structure-based pharmacophore （SBP）. LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distin- guishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore （MP） is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of per- oxisome proliferator receptor-alpha （PPARα） agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.展开更多
Human sodium-glucose cotransporter 2 (hSGLT2) is a membrane protein responsible for glucose reabsorption from the glomerular filtrate in the proximal tubule. Inhibition of hSGLT2 has been regarded as a brand new thera...Human sodium-glucose cotransporter 2 (hSGLT2) is a membrane protein responsible for glucose reabsorption from the glomerular filtrate in the proximal tubule. Inhibition of hSGLT2 has been regarded as a brand new therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM) due to its non-insulin related characteristics with less side effects. Current commercially available hSGLT2 inhibitors are all C-glycoside inhibitors. Previous studies have reported that N-glycoside inhibitors have better potential to serve as new drugs due to their good metabolic stability. In addition, non-glycoside inhibitors have been shown to exhibit the capability to overcome the existing problems of current glycoside inhibitors, including low tissue permeability, poor stability and short serum half-time. Here, we aimed to discover novel N-glycoside and non-glycoside hSGLT2 inhibitors by a combination of several computational approaches. A ligand-based pharmacophore model was generated, well validated and subsequently utilized as a 3D query to identify novel hSGLT2 inhibitors from National Cancer Institute (NCI) and Traditional Chinese Medicine (TCM) databases. Finally, one N-glycoside (NSC679207) and one non-glycoside (TCM_Piperenol_A) hSGLT2 inhibitors were successfully identified, which were proven to exhibit excellent binding affinities, pharmacokinetic properties and less toxicity than the commercially available hSGLT2 inhibitor, canagliflozin, via molecular docking, ADMET prediction, molecular dynamics (MD) simulations and binding free energy calculations. All together, our results strongly suggest that these two compounds have great potential to serve as novel hSGLT2 inhibitors for the treatment of T2DM and their efficacies may be further examined by a series of in vitro and/or in vivo bioassays.展开更多
The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compound...The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.展开更多
Protein kinases constitute a superfamily of therapeutic targets for a number of human and animal diseases that include more than 500 members accordingly to sequencing data of the human genome. The well characterized n...Protein kinases constitute a superfamily of therapeutic targets for a number of human and animal diseases that include more than 500 members accordingly to sequencing data of the human genome. The well characterized nature of protein kinases makes them excellent targets for drug development. Pharmacophore approaches have become one of the major tools in the area of drug discovery. Application of pharmacophore modeling approaches allows reducing of expensive overall cost associated with drug development project. Pharmacophore models are important functional groups of atoms in the proper spatial position for interaction with target protein. Various ligand-based and structurebased methods have been developed for pharmacophore model generation. Despite the successes in pharmacophore models generation these approaches have not reached their full capacity in application for drug discovery. In the following review, we summarize the published data on pharmacophore models for inhibitorsof tyrosine protein kinases(EGFR, HER2, VEGFR, JAK2, JAK3, Syk, ZAP-70, Tie2) and inhibitors of serine/threonine kinases(Clk, Dyrk, Chk1, IKK2, CDK1, CDK2, PLK, JNK3, GSK3, m TOR, p38 MAPK, PKB). Here, we have described the achievements of pharmacophore modeling for protein kinase inhibitors, which provide key points for further application of generated pharmacophore hypotheses in virtual screening, de novo design and lead optimization.展开更多
Searching new inhibitors of adenosine kinase （AK） is still drawing attention of experimental scientists. A better and solid model is here proposed by means of simulation methods from different ways, the direct analy...Searching new inhibitors of adenosine kinase （AK） is still drawing attention of experimental scientists. A better and solid model is here proposed by means of simulation methods from different ways, the direct analysis of receptor itself, the conventional 3D-QSAR methods and the integration of docking method and the conventional QSAR analysis.展开更多
文摘Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore （LBP） and structure-based pharmacophore （SBP）. LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distin- guishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore （MP） is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of per- oxisome proliferator receptor-alpha （PPARα） agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.
文摘Human sodium-glucose cotransporter 2 (hSGLT2) is a membrane protein responsible for glucose reabsorption from the glomerular filtrate in the proximal tubule. Inhibition of hSGLT2 has been regarded as a brand new therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM) due to its non-insulin related characteristics with less side effects. Current commercially available hSGLT2 inhibitors are all C-glycoside inhibitors. Previous studies have reported that N-glycoside inhibitors have better potential to serve as new drugs due to their good metabolic stability. In addition, non-glycoside inhibitors have been shown to exhibit the capability to overcome the existing problems of current glycoside inhibitors, including low tissue permeability, poor stability and short serum half-time. Here, we aimed to discover novel N-glycoside and non-glycoside hSGLT2 inhibitors by a combination of several computational approaches. A ligand-based pharmacophore model was generated, well validated and subsequently utilized as a 3D query to identify novel hSGLT2 inhibitors from National Cancer Institute (NCI) and Traditional Chinese Medicine (TCM) databases. Finally, one N-glycoside (NSC679207) and one non-glycoside (TCM_Piperenol_A) hSGLT2 inhibitors were successfully identified, which were proven to exhibit excellent binding affinities, pharmacokinetic properties and less toxicity than the commercially available hSGLT2 inhibitor, canagliflozin, via molecular docking, ADMET prediction, molecular dynamics (MD) simulations and binding free energy calculations. All together, our results strongly suggest that these two compounds have great potential to serve as novel hSGLT2 inhibitors for the treatment of T2DM and their efficacies may be further examined by a series of in vitro and/or in vivo bioassays.
文摘The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.
文摘Protein kinases constitute a superfamily of therapeutic targets for a number of human and animal diseases that include more than 500 members accordingly to sequencing data of the human genome. The well characterized nature of protein kinases makes them excellent targets for drug development. Pharmacophore approaches have become one of the major tools in the area of drug discovery. Application of pharmacophore modeling approaches allows reducing of expensive overall cost associated with drug development project. Pharmacophore models are important functional groups of atoms in the proper spatial position for interaction with target protein. Various ligand-based and structurebased methods have been developed for pharmacophore model generation. Despite the successes in pharmacophore models generation these approaches have not reached their full capacity in application for drug discovery. In the following review, we summarize the published data on pharmacophore models for inhibitorsof tyrosine protein kinases(EGFR, HER2, VEGFR, JAK2, JAK3, Syk, ZAP-70, Tie2) and inhibitors of serine/threonine kinases(Clk, Dyrk, Chk1, IKK2, CDK1, CDK2, PLK, JNK3, GSK3, m TOR, p38 MAPK, PKB). Here, we have described the achievements of pharmacophore modeling for protein kinase inhibitors, which provide key points for further application of generated pharmacophore hypotheses in virtual screening, de novo design and lead optimization.
文摘Searching new inhibitors of adenosine kinase （AK） is still drawing attention of experimental scientists. A better and solid model is here proposed by means of simulation methods from different ways, the direct analysis of receptor itself, the conventional 3D-QSAR methods and the integration of docking method and the conventional QSAR analysis.
