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Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug(More)
Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties play key roles in the discovery/development of drugs, pesticides, food additives, consumer products, and industrial chemicals. This information is especially useful when to conduct environmental and human hazard assessment. The most critical rate limiting step in the chemical(More)
Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP) inhibition is an important consideration, especially, during the research phase of drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP isoform. In this study, inhibitor(More)
Chemical-protein interaction (CPI) is the central topic of target identification and drug discovery. However, large scale determination of CPI is a big challenge for in vitro or in vivo experiments, while in silico prediction shows great advantages due to low cost and high accuracy. On the basis of our previous drug-target interaction prediction via(More)
Integration of viral-DNA into host chromosome mediated by the viral protein HIV-1 integrase (IN) is an essential step in the HIV-1 life cycle. In this process, human protein Lens epithelium-derived growth factor (LEDGF/p75) is discovered to function as a cellular co-factor for integration. LEDGF/p75-HIV-1 IN interaction represents an attractive target for(More)
Drug-induced liver injury (DILI) is a leading cause of acute liver failure in the US and less severe liver injury worldwide. It is also one of the major reasons of drug withdrawal from the market. Thus, DILI has become one of the most important concerns of drugs, and should be predicted in very early stage of drug discovery process. In this study, a(More)
OBJECTIVE To evaluate the association of Parkinson's disease (PD) with smoking, and determine whether gender, source of controls, dose of smoking, and year of studies modify the observed effects of smoking on PD. METHODS Available publications between 1959 and 2014 from PubMed, ScienceDirect, Springer Link and Web of Science databases were searched and(More)
Ligand-based and receptor-based methods were used to investigate the binding modes of human adenosine A(2B) antagonists. At first, pharmacophore models were developed based on 140 diverse A(2B) antagonists from literature. Meanwhile, the structural model of A(2B) receptor was built up based on the crystal structure of human A(2A) receptor and validated by(More)
Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of(More)
Mutagenicity is one of the most important end points of toxicity. Due to high cost and laboriousness in experimental tests, it is necessary to develop robust in silico methods to predict chemical mutagenicity. In this paper, a comprehensive database containing 7617 diverse compounds, including 4252 mutagens and 3365 nonmutagens, was constructed. On the(More)