A data science roadmap for open science organizations engaged in early-stage drug discovery
A data science roadmap for open science organizations engaged in early-stage drug discovery
About this item
Full title
Author / Creator
Edfeldt, Kristina , Edwards, Aled M. , Engkvist, Ola , Günther, Judith , Hartley, Matthew , Hulcoop, David G. , Leach, Andrew R. , Marsden, Brian D. , Menge, Amelie , Misquitta, Leonie , Müller, Susanne , Owen, Dafydd R. , Schütt, Kristof T. , Skelton, Nicholas , Steffen, Andreas , Tropsha, Alexander , Vernet, Erik , Wang, Yanli , Wellnitz, James , Willson, Timothy M. , Clevert, Djork-Arné , Haibe-Kains, Benjamin , Schiavone, Lovisa Holmberg and Schapira, Matthieu
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent...
Alternative Titles
Full title
A data science roadmap for open science organizations engaged in early-stage drug discovery
Authors, Artists and Contributors
Author / Creator
Edwards, Aled M.
Engkvist, Ola
Günther, Judith
Hartley, Matthew
Hulcoop, David G.
Leach, Andrew R.
Marsden, Brian D.
Menge, Amelie
Misquitta, Leonie
Müller, Susanne
Owen, Dafydd R.
Schütt, Kristof T.
Skelton, Nicholas
Steffen, Andreas
Tropsha, Alexander
Vernet, Erik
Wang, Yanli
Wellnitz, James
Willson, Timothy M.
Clevert, Djork-Arné
Haibe-Kains, Benjamin
Schiavone, Lovisa Holmberg
Schapira, Matthieu
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_d7b4037ed6df47b49327448e7112d3fb
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_d7b4037ed6df47b49327448e7112d3fb
Other Identifiers
ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-024-49777-x
