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SUMMARY:Developing biomolecular force fields in the machine learning age -
  Dr Joe Greener\, MRC Laboratory of Molecular Biology
DTSTART:20260401T100000Z
DTEND:20260401T110000Z
UID:TALK245797@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:The aim of my lab is to develop accurate\, transferable and fa
 st force fields for biomolecular simulation. I will describe two projects.
  Firstly\, the development of an implicit solvent model using the techniqu
 e of differentiable simulation to better describe intrinsically disordered
  proteins (IDPs). This force field\, GB99dms\, fixes the over-compaction o
 f IDPs in implicit solvent and allows fast conformational exploration. Sec
 ondly\, the training of an all-atom biomolecular force field without refer
 ence to previous parameters. This model\, Garnet\, uses a graph neural net
 work for continuous atom typing and is trained on quantum mechanical\, con
 densed phase and protein NMR data. The force field shows comparable perfor
 mance to existing\, manually-parameterised force fields and is competitive
  at predicting binding free energies across a range of targets.\n
LOCATION:Pfizer Lecture Theatre - Yusuf Hamied Department of Chemistry
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