GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Liu, Xian  (2)
  • Biodiversity Research  (2)
Material
Language
Years
FID
  • Biodiversity Research  (2)
  • 1
    In: Bioinformatics, Oxford University Press (OUP), Vol. 31, No. 12 ( 2015-06-15), p. 2049-2051
    Abstract: Motivation: Discovering the relevant therapeutic targets for drug-like molecules, or their unintended ‘off-targets’ that predict adverse drug reactions, is a daunting task by experimental approaches alone. There is thus a high demand to develop computational methods capable of detecting these potential interacting targets efficiently. Results: As biologically annotated chemical data are becoming increasingly available, it becomes feasible to explore such existing knowledge to identify potential ligand–target interactions. Here, we introduce an online implementation of a recently published computational model for target prediction, TarPred, based on a reference library containing 533 individual targets with 179 807 active ligands. TarPred accepts interactive graphical input or input in the chemical file format of SMILES. Given a query compound structure, it provides the top ranked 30 interacting targets. For each of them, TarPred not only shows the structures of three most similar ligands that are known to interact with the target but also highlights the disease indications associated with the target. This information is useful for understanding the mechanisms of action and toxicities of active compounds and can provide drug repositioning opportunities. Availability and implementation: TarPred is available at: http://www.dddc.ac.cn/tarpred. Contact:  hljiang@simm.ac.cn or myzheng@simm.ac.cn
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2015
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    Online Resource
    Online Resource
    Cambridge University Press (CUP) ; 2015
    In:  Quarterly Reviews of Biophysics Vol. 48, No. 4 ( 2015-11), p. 488-515
    In: Quarterly Reviews of Biophysics, Cambridge University Press (CUP), Vol. 48, No. 4 ( 2015-11), p. 488-515
    Abstract: In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.
    Type of Medium: Online Resource
    ISSN: 0033-5835 , 1469-8994
    Language: English
    Publisher: Cambridge University Press (CUP)
    Publication Date: 2015
    detail.hit.zdb_id: 1474559-8
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...