In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences
نویسندگان
چکیده
منابع مشابه
Prediction of Drug-Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures.
Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel co...
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Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way....
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Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to compleme...
متن کاملPrediction of drug–target interaction networks from the integration of chemical and genomic spaces
MOTIVATION The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug-target interactions efficiently. RESULTS In this article, we characterize four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-pr...
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ژورنال
عنوان ژورنال: Scientific Reports
سال: 2017
ISSN: 2045-2322
DOI: 10.1038/s41598-017-10724-0