[Show abstract][Hide abstract] ABSTRACT: Hepatitis C virus (HCV) is an infectious virus that can cause serious illnesses. Only a few drugs have been reported to effectively treat hepatitis C. To have greater diversity in drug choice and better treatment options, it is necessary to develop more drugs to treat the infection. However, it is time-consuming and expensive to discover candidate drugs using experimental methods, and computational methods may complement experimental approaches as a preliminary filtering process. This type of approach was proposed by using known chemical-chemical interactions to extract interactive compounds with three known drug compounds of HCV, and the probabilities of these drug compounds being able to treat hepatitis C were calculated using chemical-protein interactions between the interactive compounds and HCV target genes. Moreover, the randomization test and expectation-maximization (EM) algorithm were both employed to exclude false discoveries. Analysis of the selected compounds, including acyclovir and ganciclovir, indicated that some of these compounds had potential to treat the HCV. Hopefully, this proposed method could provide new insights into the discovery of candidate drugs for the treatment of HCV and other diseases.
PLoS ONE 09/2014; 9(9):e107767. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Aim:A large number of drug-induced long QT syndromes are ascribed to blockage of hERG potassium channels. The aim of this study was to construct novel computational models to predict compounds blocking hERG channels.Methods:Doddareddy's hERG blockage data containing 2644 compounds were used, which divided into training (2389) and test (255) sets. Laplacian-corrected Bayesian classification models were constructed using Discovery Studio. The models were internally validated with the training set of compounds, and then applied to the test set for validation. Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation.Results:A Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation.Conclusion:The novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.
[Show abstract][Hide abstract] ABSTRACT: Graphical abstract
Twenty novel chlorantraniliprole derivatives were synthesized and their insecticidal activities against diamondback moth were evaluated with chlorantraniliprole and indoxacarb as control. All compounds except 8h, 8p and 8t exibited varying degree of activityies against diamondback moth. Especially, compound 8c, 8i, 8k and 8l dispayed good insecticidal activities with similar to, even better than that of indoxacarb. Molecular docking was used for the first time to estimate our proposed receptor and the result indicated the selected protein might have specific binding site of chlorantraniliprole on ryanodine receptor.
[Show abstract][Hide abstract] ABSTRACT: In the Anatomical Therapeutic Chemical (ATC) classification system, therapeutic drugs are divided into 14 main classes according to the organ or system on which they act and their chemical, pharmacological and therapeutic properties. This system, recommended by the World Health Organization (WHO), provides a global standard for classifying medical substances and serves as a tool for international drug utilization research to improve quality of drug use. In view of this, it is necessary to develop effective computational prediction methods to identify the ATC-class of a given drug, which thereby could facilitate further analysis of this system. In this study, we initiated an attempt to develop a prediction method and to gain insights from it by utilizing ontology information of drug compounds. Since only about one-fourth of drugs in the ATC classification system have ontology information, a hybrid prediction method combining the ontology information, chemical interaction information and chemical structure information of drug compounds was proposed for the prediction of drug ATC-classes. As a result, by using the Jackknife test, the 1st prediction accuracies for identifying the 14 main ATC-classes in the training dataset, the internal validation dataset and the external validation dataset were 75.90%, 75.70% and 66.36%, respectively. Analysis of some samples with false-positive predictions in the internal and external validation datasets indicated that some of them may even have a relationship with the false-positive predicted ATC-class, suggesting novel uses of these drugs. It was conceivable that the proposed method could be used as an efficient tool to identify ATC-classes of novel drugs or to discover novel uses of known drugs.
[Show abstract][Hide abstract] ABSTRACT: Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1(st) order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the 'wrong' predicted indications, indicating that some 'wrong' drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.
PLoS ONE 02/2014; 9(2):e87791. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD50, is frequently used as a general indicator of a substance's acute toxicity, and there is a high demand on developing non-animal-based prediction of LD50. Unfortunately, it is difficult to accurately predict compound LD50 using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes.
Journal of Cheminformatics 01/2014; 6(1):26. · 4.54 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The human uridine diphosphate (UDP)-glucuronosyltransferase (UGT) enzyme family catalyzes the glucuronidation of the glycosyl group of a nucleotide sugar to an acceptor compound (substrate), which is the most common conjugation pathway that serves to protect the organism from the potential toxicity of xenobiotics. Moreover it could affect the pharmacological profile of a drug. Therefore it is important to identify the metabolically labile sites for glucuronidation.
In the present study, we developed four in silico models to predict sites of glucuronidation, for four major sites of metabolism (SOM) functional groups, i.e., aliphatic hydroxyl, aromatic hydroxyl, carboxylic acid, or amino nitrogen, respectively. According to the mechanism of glucuronidation, a series of "local" and "global" molecular descriptors characterizing the atomic reactivity, bonding strength and physical chemical properties were calculated and selected with a genetic algorithm based feature selection approach. The constructed support vector machine (SVM) classification models show good prediction performance, with the balanced accuracy ranging from 0.88 to 0.96 on test set. For further validation, our models can successfully identify 84% of experimentally observed SOMs for an external test set containing 54 molecules.
The software somugt based on our models is available at www.dddc.ac.cn/adme/jlpeng/somugtwin32.zip.
email@example.com; firstname.lastname@example.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
[Show abstract][Hide abstract] ABSTRACT: Non-covalent interactions like hydrogen bonding, hydrophobic interactions and salt bridges, have been our primary focus in designing and optimizing drugs. Recently, there is mounting evidence that non-covalent interactions involving aromatic rings are also potent forces for the recognition between small drug-like compounds and their targets. Understanding of these interactions and their physical origin are of significant interest for improving the current drug design strategy. Hence, numerous efforts have been devoted to elucidating the structural, geometrical, energetic, and thermodynamic properties of these interactions, which include π-π, cation-π and anion-π interactions. In this review, we established a framework to systematically understand the structural basis and physicochemical properties of the aromatic interactions at the binding interface of protein-ligand complexes. Firstly, we presented an introduction including the definition, universality, energy components, geometry conformations and substituent effects of these interactions. Secondly, we retrospected the widely employed computational approaches for studying these interactions, including quantum mechanical calculations and crystallographic data mining. Finally, we illustrated with several representative protein-ligand systems to show how the aromatic interactions contribute to the design and optimization of ligand in both affinity and specificity.
Current pharmaceutical design 06/2013; · 4.41 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Acquired immune deficiency syndrome (AIDS) is a severe infectious disease that causes a large number of deaths every year. Traditional anti-AIDS drugs directly targeting the HIV-1 encoded enzymes including reverse transcriptase (RT), protease (PR) and integrase (IN) usually suffer from drug resistance after a period of treatment and serious side effects. In recent years, the emergence of numerous useful information of protein-protein interactions (PPI) in the HIV life cycle and related inhibitors makes PPI a new way for antiviral drug intervention. In this study, we identified 26 core human proteins involved in PPI between HIV-1 and host, that have great potential for HIV therapy. In addition, 280 chemicals that interact with three HIV drugs targeting human proteins can also interact with these 26 core proteins. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying novel anti-HIV drugs.
PLoS ONE 06/2013; 8(6):e65207. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Drug-target interaction is a key research topic in drug discovery since correct identification of target proteins of drug candidates can help screen out those with unacceptable toxicities, thereby saving expense. In this study, we developed a novel computational approach to predicting drug target groups that may reduce the number of candidate target proteins associated with a query drug. A benchmark dataset, consisting of 3,028 drugs assigned within nine categories, was constructed by collecting data from KEGG. The nine categories are (1) G protein-coupled receptors, (2) cytokine receptors, (3) nuclear receptors, (4) ion channels, (5) transporters, (6) enzymes, (7) protein kinases, (8) cellular antigens and (9) pathogens. The proposed method combines the data gleaned from chemical-chemical similarities, chemical-chemical connections and chemical-protein connections to allocate drugs to each of the nine target groups. A jackknife test applied to the training dataset that was constructed from the benchmark dataset, provided an overall correct prediction rate of 87.45%, as compared to 87.79% for the test dataset that was constructed by randomly selecting 10% of samples from the benchmark dataset. These prediction rates are much higher than the 11.11% achieved by random guesswork. These promising results suggest that the proposed method can become a useful tool in identifying drug target groups.
Biochimica et Biophysica Acta 05/2013; 1844(1):207-213. · 4.66 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more likely to share similar toxicity profiles. According to their interaction confidence scores, the order from the most likely toxicity to the least was obtained for each compound. Ten test groups, each of them containing one training dataset and one test dataset, were constructed from a benchmark dataset consisting of 17,233 compounds. By a Jackknife test on each of these test groups, the 1 order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, substantially higher than the rate of 25.43% achieved by random guesses. Encouraged by the promising results, we expect that our method will become a useful tool in screening out drugs with high toxicity.
PLoS ONE 02/2013; 8(2):e56517. · 3.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Membrane transporters, including two members: ATP-binding cassette (ABC) transporters and solute carrier (SLC) transporters are proteins that play important roles to facilitate molecules into and out of cells. Consequently, these transporters can be major determinants of the therapeutic efficacy, toxicity and pharmacokinetics of a variety of drugs. Considering the time and expense of bio-experiments taking, research should be driven by evaluation of efficacy and safety. Computational methods arise to be a complementary choice. In this article, we provide an overview of the contribution that computational methods made in transporters field in the past decades. At the beginning, we present a brief introduction about the structure and function of major members of two families in transporters. In the second part, we focus on widely used computational methods in different aspects of transporters research. In the absence of a high-resolution structure of most of transporters, homology modeling is a useful tool to interpret experimental data and potentially guide experimental studies. We summarize reported homology modeling in this review. Researches in computational methods cover major members of transporters and a variety of topics including the classification of substrates and/or inhibitors, prediction of protein-ligand interactions, constitution of binding pocket, phenotype of non-synonymous single-nucleotide polymorphisms, and the conformation analysis that try to explain the mechanism of action. As an example, one of the most important transporters P-gp is elaborated to explain the differences and advantages of various computational models. In the third part, the challenges of developing computational methods to get reliable prediction, as well as the potential future directions in transporter related modeling are discussed.
Current Medicinal Chemistry 01/2013; · 3.72 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Phase I metabolism is an important consideration in drug discovery because it profoundly affects the toxicity and activity profile of a drug candidate. In these metabolic processes, CYP450 family is responsible for the majority of biotransformation events. However, it is still an important challenge to predict sites of metabolism (SOM) of a new chemical entity due to the complex reaction mechanism and variety in CYP450 enzymes. SOMEViz is an online service designed for predicting and visualizing human cytochromes P450 (CYP450)-mediated sites of metabolism (SOM) of a molecule, on the basis of a previously reported model. The service provides an access for predicting sites of metabolism of molecules with reasonable accuracy, and predicted results are shown in a user-friendly as well as interactive way, which may help chemists explore metabolism properties of chemicals in the early stage of drug discovery. The web-based GUI of SOMEViz offers user a straightforward way to manage and visualize the sites of metabolism (SOM) prediction results. The service and examples are available free of charge at http://www.dddc.ac.cn/some.
Protein and Peptide Letters 09/2012; 19(9):905-9. · 1.74 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Carcinogenicity is an important toxicological endpoint that poses high concern to drug discovery. In this study, we developed a method to extract structural alerts (SAs) and modulating factors of carcinogens on the basis of statistical analyses. First, the Gaston algorithm, a frequent subgraph mining method, was used to detect substructures that occurred at least six times. Then, a molecular fragments tree was built and pruned to select high-quality SAs. The p-value of the parent node in the tree and that of its children nodes were compared, and the nodes that had a higher statistical significance in binomial tests were retained. Finally, modulating factors that suppressed the toxic effects of SAs were extracted by three self-defining rules. The accuracy of the 77 SAs plus four SA/modulating factor pairs model for the training set, and the test set was 0.70 and 0.65, respectively. Our model has higher predictive ability than Benigni's model, especially in the test set. The results highlight that this method is preferable in terms of prediction accuracy, and the selected SAs are useful for prediction as well as interpretation. Moreover, our method is convenient to users in that it can extract SAs from a database using an automated and unbiased manner that does not rely on a priori knowledge of mechanism of action.
Journal of Chemical Information and Modeling 07/2012; 52(8):1994-2003. · 4.30 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Skin sensitization is an important toxic endpoint in the risk assessment of chemicals. In this paper, structure-activity relationships analysis was performed on the skin sensitization potential of 357 compounds with local lymph node assay data. Structural fragments were extracted by GASTON (GrAph/Sequence/Tree extractiON) from the training set. Eight fragments with accuracy significantly higher than 0.73 (p<0.1) were retained to make up an indicator descriptor fragment. The fragment descriptor and eight other physicochemical descriptors closely related to the endpoint were calculated to construct the recursive partitioning tree (RP tree) for classification. The balanced accuracy of the training set, test set I, and test set II in the leave-one-out model were 0.846, 0.800, and 0.809, respectively. The results highlight that fragment-based RP tree is a preferable method for identifying skin sensitizers. Moreover, the selected fragments provide useful structural information for exploring sensitization mechanisms, and RP tree creates a graphic tree to identify the most important properties associated with skin sensitization. They can provide some guidance for designing of drugs with lower sensitization level.