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AI3SD, OSM & RSC-CICAG Predicting the activity of Drug Candidates when there is no target Workshop Report

AI3SD, OSM & RSC-CICAG Predicting the activity of Drug Candidates when there is no target Workshop Report
AI3SD, OSM & RSC-CICAG Predicting the activity of Drug Candidates when there is no target Workshop Report
This one-day meeting brought together two highly topical areas of drug discovery. Firstly, the application of machine learning/artificial intelligence (ML/AI) approaches to the discovery of new drug leads and secondly, programs where the biological target is not clearly established - so-called phenotypic drug discovery. Whilst there have been a number of publications describing AI or machine learning approaches in drug discovery many use historical data sets that have been carefully cleaned and validated. Unfortunately, real world experimental data from a phenotypic screen is rarely clean and tidy and presents significant challenges to model builders.
AI3SD, OSM, RSC, Artificial Intelligence, Drug Discovery, Machine Learning
16
University of Southampton
Swain, Chris
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Todd, Matthew
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Kanza, Samantha
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Frey, Jeremy G.
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Swain, Chris
4c1b02f8-af57-455a-b7b3-d826cb21c884
Todd, Matthew
7e2a5b37-a4da-4e91-9e43-1a8ee0c0ad78
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f

Swain, Chris , Todd, Matthew, Kanza, Samantha and Frey, Jeremy G. (eds.) (2020) AI3SD, OSM & RSC-CICAG Predicting the activity of Drug Candidates when there is no target Workshop Report (AI3SD-Event-Series, , (doi:10.5258/SOTON/P0020), 16) University of Southampton 19pp.

Record type: Monograph (Project Report)

Abstract

This one-day meeting brought together two highly topical areas of drug discovery. Firstly, the application of machine learning/artificial intelligence (ML/AI) approaches to the discovery of new drug leads and secondly, programs where the biological target is not clearly established - so-called phenotypic drug discovery. Whilst there have been a number of publications describing AI or machine learning approaches in drug discovery many use historical data sets that have been carefully cleaned and validated. Unfortunately, real world experimental data from a phenotypic screen is rarely clean and tidy and presents significant challenges to model builders.

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AI3SD_Event_Series_Report-16_PredictingTheActivityOfDrugCandidatesWhenThereIsNoTarget - Version of Record
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Published date: 10 February 2020
Keywords: AI3SD, OSM, RSC, Artificial Intelligence, Drug Discovery, Machine Learning

Identifiers

Local EPrints ID: 438123
URI: http://eprints.soton.ac.uk/id/eprint/438123
PURE UUID: e6be754c-0813-4da9-adff-24fdca737001
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: 02 Mar 2020 17:30
Last modified: 19 May 2020 00:59

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