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BEGIN:VEVENT
DTSTAMP:20260404T201315Z
DTSTART;VALUE=DATE-TIME:20221109T130000
DTEND;VALUE=DATE-TIME:20221109T140000
SUMMARY:New computational methods for charting chemical degradation space
 .
TZID:Europe/London
UID:20221109-8a1785d8843d176a01845246f6f84b18@warwick.ac.uk
CREATED:20221107T132813Z
DESCRIPTION:Abstract Stability is among the most central chemical propert
 ies for which no predictive methods exist. Characterizing stability is a
 lso a practical bottleneck in many chemical and materials development ap
 plications. For example\, the inability to predict stability has made it
  commonplace for computational screening projects to conclude with slow 
 and manual expert curation of practical structures by organic chemists. 
 In this talk\, I’ll review the challenges associated with modeling stabi
 lity and argue that the reaction prediction problem is the pivotal but s
 olvable obstacle. The second half of the talk will focus on methods for 
 accelerating the identification of degradants from high-throughput stabi
 lity experiments. The challenge here is that degradation chemistry often
  yields complex product mixtures that are not found in databases. To add
 ress this\, we are developing deductive machine learning models capable 
 of ingesting multiple information sources to deduce degradants. Microsof
 t Teams meeting Join on your computer or mobile app Click here to join t
 he meeting Meeting ID: 377 186 058 539 Passcode: JZTbdZ Download Teams |
  Join on the web
LOCATION:MS TEams
URL:
ATTACH:
CATEGORIES:Research,Research Seminar
LAST-MODIFIED:20221107T132813Z
ORGANIZER;CN=Elise Bennett:
END:VEVENT
END:VCALENDAR