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UID:363@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250328T140000
DTEND;TZID=Europe/Paris:20250328T140000
DTSTAMP:20260828T094455Z
URL:https://isdm.umontpellier.fr/events/learning-structural-biology-3/
SUMMARY:Learning structural biology
DESCRIPTION:Room 02.124\, Building 5\, St Priest campus\n\nMachine Learning
  in Montpellier\, Theory &amp\; Practice - Vincent Mallet (Ecole des Mines
  de Paris)\n\nLearning on the 3D structure of biomolecules resulted in sev
 eral breakthroughs\, for instance the de-novo design of potent binders or 
 functional luciferases\, eventually leading to a Nobel Prize. Most of this
  work relies on modeling the structure of protein as graphs\, with well-es
 tablished tools. In my talk I will start by introducing the topic and revi
 ewing recent methods. I will then present our recent results on the coordi
 nated use of graphs\, sequence and surface representations to model protei
 n structure with machine learning. We show that despite disappointing resu
 lts in isolation\, surface methods used in addition to others display a sy
 nergistic effect. I will also discuss ongoing work to enhance this method\
 , as well as apply it to homologs mining and binder design. About the pres
 enter: Vincent Mallet is a researcher working on modeling the structure of
  biomolecules with machine learning\, with applications in structural biol
 ogy and drug design. His two main lines of work are surface-based represen
 tations for proteins and graph-based representations for RNA. He is part o
 f the CBIO team\, in Mines Paris PSL and Institut Curie. Previously\, he w
 as a postdoc with Maks Ovsjanikov working on geometric deep learning. He d
 id his PhD with Jean-Philippe Vert and Michael Nilges on equivariant metho
 ds and drug design.\n\nMachine Learning in Montpellier\, Theory &amp\; Pra
 ctice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
LOCATION:Room 02.124 Building 5\, St Priest Campus\, Montpellier\, 34000\, 
 France
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TZID:Europe/Paris
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DTSTART:20241027T020000
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