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SUMMARY:Machine Learning-Based Design of Proteins and Small Molecules
DTSTART:20200129T100000
DTEND:20200129T110000
DTSTAMP:20260511T085337Z
UID:63a5ee7c2f8418585051bd212437142a55f2d66a2d4e2bfc9d900a3f
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Jennifer Listgarten\, University of California\, Berkele
 y\, CA (USA)\nBioengineering Mini-Symposium - "A Morning in Machine-Learni
 ng and Protein Prediction and Design" - Talk One\n(Talk Two: see here)\n 
 \nAbstract:\nWith the advent of more and more high-throughput technologies
  to measure protein properties of interest such as binding\, expression\, 
 fluorescence\, the time for machine learning to act synergistically with p
 rotein design is here. I will describe our work on accelerating the design
 /optimization of proteins (and small molecules) with machine learning appr
 oaches - a sort of in silico approach to the method of Directed Evolution 
 which won the 2018 Nobel prize in Chemistry.\n\nZoom link for attending re
 motely: https://epfl.zoom.us/j/282485607
LOCATION:SV 1717 https://plan.epfl.ch/?room==SV%201717
STATUS:CONFIRMED
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