The University of Southampton
University of Southampton Institutional Repository

AI3SD & MDC AI in Drug Discovery & Drug Safety Workshop Report 2019

AI3SD & MDC AI in Drug Discovery & Drug Safety Workshop Report 2019
AI3SD & MDC AI in Drug Discovery & Drug Safety Workshop Report 2019
Drug discovery is a long and long-term scientific investigation involving interdisciplinary research methods coupled with large heterogeneous datasets. The research and data space in this area is vast, and AI3SD and MDC believe that the use of AI and machine learning technologies can help spur on advances in this domain. The current workshop was designed to draw together those with a keen interest in using AI and machine learning technologies in the domain of drug discovery, both to aid future drug discovery, and to help improve drug safety. AI3SD firmly believes that interdisciplinary collaboration is the key to many of these advances. At the workshop, keynote talks were interspersed with general group discussions and working groups around the key topics that arose.
AI3SD, Workshop Report, Drug Discovery, drug safety, AI, Artificial intelligence, Machine Learning
7
University of Southampton
Warr, Wendy
96fed7e7-c301-4192-8ba0-83ab354c7e7a
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Warr, Wendy
96fed7e7-c301-4192-8ba0-83ab354c7e7a
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f

Warr, Wendy , Kanza, Samantha and Frey, Jeremy G. (eds.) (2019) AI3SD & MDC AI in Drug Discovery & Drug Safety Workshop Report 2019 (AI3SD-Event-Series, 7) University of Southampton 11pp. (doi:10.5258/SOTON/P0008).

Record type: Monograph (Project Report)

Abstract

Drug discovery is a long and long-term scientific investigation involving interdisciplinary research methods coupled with large heterogeneous datasets. The research and data space in this area is vast, and AI3SD and MDC believe that the use of AI and machine learning technologies can help spur on advances in this domain. The current workshop was designed to draw together those with a keen interest in using AI and machine learning technologies in the domain of drug discovery, both to aid future drug discovery, and to help improve drug safety. AI3SD firmly believes that interdisciplinary collaboration is the key to many of these advances. At the workshop, keynote talks were interspersed with general group discussions and working groups around the key topics that arose.

Text
AI3SD-Event-Series_Report-7_AIinDrugDiscoveryAndDrugSafetyWorkshop - Version of Record
Available under License Creative Commons Attribution.
Download (1MB)
Text
AI3SD-Event-Series_Report-7_AIinDrugDiscoveryAndDrugSafetyWorkshop (previous version) - Version of Record
Restricted to Repository staff only
Available under License Creative Commons Attribution.
Text
AI3SD-Event-Series_Report-7_AIinDrugDiscoveryAndDrugSafetyWorkshop-V2 - Version of Record
Available under License Creative Commons Attribution.
Download (1MB)

More information

In preparation date: 6 March 2019
Published date: 28 June 2019
Keywords: AI3SD, Workshop Report, Drug Discovery, drug safety, AI, Artificial intelligence, Machine Learning

Identifiers

Local EPrints ID: 432067
URI: http://eprints.soton.ac.uk/id/eprint/432067
PURE UUID: 2d34556a-f964-49d8-b41e-eeee2de4ae74
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 28 Jun 2019 16:30
Last modified: 16 Mar 2024 04:36

Export record

Altmetrics

Contributors

Editor: Samantha Kanza ORCID iD
Author: Wendy Warr
Editor: Jeremy G. Frey ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×