Zobrazeno 1 - 5
of 5
pro vyhledávání: '"in silico structure generation"'
Publikováno v:
Journal of Cheminformatics
Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-20 (2021)
Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-20 (2021)
Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive
Autor:
Leen Kalash, Marcel Bermudez, Graham Ladds, Ian Winfield, Dewi Safitri, Sabrina Carvalho, Robert C. Glen, Andreas Bender
Publikováno v:
Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-17 (2021)
Journal of Cheminformatics
Journal of Cheminformatics
Enhanced/prolonged cAMP signalling has been suggested as a suppressor of cancer proliferation. Interestingly, two key modulators that elevate cAMP, the A2A receptor (A2AR) and phosphodiesterase 10A (PDE10A), are differentially co-expressed in various
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::46a6d65238905c66462260ad45998e0f
Publikováno v:
Metabolites, Vol 6, Iss 2, p 17 (2016)
Metabolite structure identification remains a significant challenge in nontargeted metabolomics research. One commonly used strategy relies on searching biochemical databases using exact mass. However, this approach fails when the database does not c
Externí odkaz:
https://doaj.org/article/ffc439b59be541c7b6e797042d03640e
Publikováno v:
Metabolites; Volume 6; Issue 2; Pages: 17
Metabolites, Vol 6, Iss 2, p 17 (2016)
Metabolites
Metabolites, Vol 6, Iss 2, p 17 (2016)
Metabolites
Metabolite structure identification remains a significant challenge in nontargeted metabolomics research. One commonly used strategy relies on searching biochemical databases using exact mass. However, this approach fails when the database does not c
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