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SUMMARY:Exploring and implementing molecular counterfactuals for chemical 
 toxicology prediction - Katie Beckwith\, University of Cambridge
DTSTART:20230315T143000Z
DTEND:20230315T150000Z
UID:TALK193687@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:As access to computing power\, open source data and machine le
 arning packages becomes greater\, as does the growth of molecular machine 
 learning (MML). Such technology can attempt to provide answers to a wide r
 ange of chemistry based questions. Most  techniques with good performance 
 are however unable to answers one key question “but why?”. Much of sci
 ence is achieved through pondering such a question so the ability to provi
 de an answer to this question is something which is strived for within MML
 . Explainable AI has been introduced into the realm of MML with the goal o
 f giving reasons why. In this work molecular counterfactuals have been imp
 lemented and explored as an explainable AI technique\, as well as an inves
 tigation on the STONED algorithm and how augmentation of this can impact t
 he quality of produced counterfactuals.
LOCATION:Unilever Lecture Theatre\, Yusuf Hamied Department of Chemistry
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