Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases
- Submitting institution
-
Goldsmiths' College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2029
- Type
- D - Journal article
- DOI
-
10.1098/rsif.2014.1289
- Title of journal
- Journal of the Royal Society, Interface
- Article number
- 20141289
- First page
- -
- Volume
- 12
- Issue
- 104
- ISSN
- 1742-5662
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2015
- URL
-
http://research.gold.ac.uk/id/eprint/23611/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
11
- Research group(s)
-
-
- Citation count
- 40
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper reports on an advanced AI system ‘Eve’ for (semi-) automated discovery of drug leads. Eve is a complex integrated system that includes advanced robotic laboratory and Machine Learning software. The significance of this work is in supporting repeated cycles of planning and execution of experiments and advancing active Machine Learning. The proposed approach is faster and more economical than standard drug screening. The reported results were featured in the technology news (e.g. http://www.moreyearslesstears.com/robot-scientist-eve-could-boost-search-for-new-drugs/) and were used for technological advances in industry. The paper was cited in the OECD Science, Technology and Innovation Outlook 2018 (ISBN 978-92-64-30757-5).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -